Date: (Tue) Apr 21, 2015

Introduction:

Data: Source: Training: https://courses.edx.org/c4x/MITx/15.071x_2/asset/ClaimsData.csv.zip
New:
Time period:

Synopsis:

Based on analysis utilizing <> techniques, :

[](.png)

Potential next steps include:

  • Probability handling for multinomials vs. desired binomial outcome
  • ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
  • extensions toward multiclass classification are scheduled for the next release

  • Skip trControl.method=“cv” for dummy classifier ?
  • Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
  • glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented

  • Compare glb_sel_mdl vs. glb_fin_mdl:
    • varImp
    • Prediction differences (shd be minimal ?)
  • Prediction accuracy scatter graph:
  • Add tiles (raw vs. PCA)
  • Use shiny for drop-down of “important” features
  • Use plot.ly for interactive plots ?

  • Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
  • Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
  • Parameterize glb_analytics_pn
  • Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
  • Replicate myfit_mdl_classification features in myfit_mdl_regression
  • Do non-glm methods handle interaction terms ?
  • f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
  • Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
  • Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
  • Incorporate code chunks in print_sessionInfo
  • Test against
    • projects in github.com/bdanalytics
    • lectures in jhu-datascience track

Analysis:

rm(list=ls())
set.seed(12345)
options(stringsAsFactors=FALSE)
source("~/Dropbox/datascience/R/mydsutils.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
# Gather all package requirements here
#suppressPackageStartupMessages(require())
#packageVersion("caret")

#require(sos); findFn("pinv", maxPages=2, sortby="MaxScore")

# Analysis control global variables
glb_trnng_url <- "https://courses.edx.org/c4x/MITx/15.071x_2/asset/ClaimsData.csv.zip"
glb_newdt_url <- "<newdt_url>"
glb_is_separate_newent_dataset <- FALSE    # or TRUE
glb_split_entity_newent_datasets <- TRUE   # or FALSE
glb_split_newdata_method <- "sample"          # "condition" or "sample"
glb_split_newdata_condition <- "<col_name> <condition_operator> <value>"    # or NULL
glb_split_newdata_size_ratio <- 0.4               # > 0 & < 1
glb_split_sample.seed <- 88               # or any integer
glb_max_obs <- 500000 # or NULL

glb_is_regression <- FALSE; glb_is_classification <- TRUE

glb_rsp_var_raw <- "bucket2009"

# for classification, the response variable has to be a factor
#   especially for random forests (method="rf")
glb_rsp_var <- "bucket2009.fctr"    # or glb_rsp_var_raw

# if the response factor is based on numbers e.g (0/1 vs. "A"/"B"), 
#   caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- function(raw) {
    as.factor(paste0("B", raw))
} # or NULL
#glb_map_rsp_raw_to_var(c(1, 2, 3, 4, 5))

glb_map_rsp_var_to_raw <- function(var) {
    as.numeric(var)
} # or NULL
#glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(c(1, 2, 3, 4, 5)))

if ((glb_rsp_var != glb_rsp_var_raw) & is.null(glb_map_rsp_raw_to_var))
    stop("glb_map_rsp_raw_to_var function expected")

glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # model_id is appended later
glb_id_vars <- NULL # or c("<id_var>")

# List transformed vars  
glb_exclude_vars_as_features <- c("bucket2008.fctr")     # or c(NULL)
# List feats that shd be excluded due to known causation by prediction variable
if (glb_rsp_var_raw != glb_rsp_var)
    glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, 
                                            glb_rsp_var_raw)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, 
                                      c("reimbursement2009")) # or NULL
# List output vars (useful during testing in console)          
# glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, 
#                         grep(glb_rsp_var_out, names(glb_entity_df), value=TRUE)) 

glb_impute_na_data <- FALSE            # or TRUE
glb_mice_complete.seed <- 144               # or any integer

# Classification
#   rpart:  .rnorm messes with the models badly
#           caret creates dummy vars for factor feats which messes up the tuning
#               - better to feed as.numeric(<feat>.fctr) to caret 

#glb_models_method_vctr <- c("glm", "rpart", "rf")   # Binomials
glb_models_method_vctr <- c("rpart", "rf")          # Multinomials
#glb_models_method_vctr <- c("rpart")          # Multinomials - this exercise

glb_models_lst <- list(); glb_models_df <- data.frame()
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- c("bucket2008.fctr") # or NULL

glb_model_metric_terms <- matrix(c(
                              0,1,2,3,4,
                              2,0,1,2,3,
                              4,2,0,1,2,
                              6,4,2,0,1,
                              8,6,4,2,0
                          ), byrow=TRUE, nrow=5)    # or NULL
glb_model_metric <- "loss.error" # or NULL
glb_model_metric_maximize <- FALSE # or NULL (TRUE is not the default for both classification & regression) 
glb_model_metric_smmry <- function(data, lev=NULL, model=NULL) {
    confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
    #print(confusion_mtrx)
    #print(confusion_mtrx * glb_model_metric_terms)
    metric <- sum(confusion_mtrx * glb_model_metric_terms) / nrow(data)
    names(metric) <- glb_model_metric
    return(metric)
}

glb_tune_models_df <- 
   rbind(
    data.frame(parameter="cp", min=0.00005, max=0.00005, by=0.000005),
                              #min=0.00004; max=0.00006; by=0.000005
    #data.frame(parameter="mtry", min=2, max=4, by=1),
    data.frame(parameter="dummy", min=2, max=4, by=1)
        ) 
# or NULL
glb_n_cv_folds <- 5 # or NULL

glb_clf_proba_threshold <- NULL # 0.5

# Model selection criteria
# For binomial classification add AIC
glb_model_sel_frmla <- formula(paste0("~ ", 
    ifelse(!is.null(glb_model_metric), 
        paste0(ifelse(!glb_model_metric_maximize, "+min.", "-max."), 
               paste0(glb_model_metric, ".OOB")), 
           ""), " -max.Accuracy.OOB -max.Kappa.OOB"))

glb_sel_mdl_id <- "All.X.lser.ys.cp.4015.rpart" # or NULL
glb_fin_mdl_id <- glb_sel_mdl_id # or "Final"

# Depict process
glb_analytics_pn <- petrinet(name="glb_analytics_pn",
                        trans_df=data.frame(id=1:6,
    name=c("data.training.all","data.new",
           "model.selected","model.final",
           "data.training.all.prediction","data.new.prediction"),
    x=c(   -5,-5,-15,-25,-25,-35),
    y=c(   -5, 5,  0,  0, -5,  5)
                        ),
                        places_df=data.frame(id=1:4,
    name=c("bgn","fit.data.training.all","predict.data.new","end"),
    x=c(   -0,   -20,                    -30,               -40),
    y=c(    0,     0,                      0,                 0),
    M0=c(   3,     0,                      0,                 0)
                        ),
                        arcs_df=data.frame(
    begin=c("bgn","bgn","bgn",        
            "data.training.all","model.selected","fit.data.training.all",
            "fit.data.training.all","model.final",    
            "data.new","predict.data.new",
            "data.training.all.prediction","data.new.prediction"),
    end  =c("data.training.all","data.new","model.selected",
            "fit.data.training.all","fit.data.training.all","model.final",
            "data.training.all.prediction","predict.data.new",
            "predict.data.new","data.new.prediction",
            "end","end")
                        ))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())
## Loading required package: grid

glb_analytics_avl_objs <- NULL

glb_script_tm <- proc.time()
glb_script_df <- data.frame(chunk_label="import_data", 
                            chunk_step_major=1, chunk_step_minor=0,
                            elapsed=(proc.time() - glb_script_tm)["elapsed"])
print(tail(glb_script_df, 2))
##         chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed import_data                1                0   0.003

Step 1: import data

glb_entity_df <- myimport_data(
    url=glb_trnng_url, 
    comment="glb_entity_df", force_header=TRUE,
    print_diagn=(glb_is_separate_newent_dataset | 
                !glb_split_entity_newent_datasets))
## [1] "Reading file ./data/ClaimsData.csv..."
## [1] "dimensions of data in ./data/ClaimsData.csv: 458,005 rows x 16 cols"
if (glb_is_separate_newent_dataset) {
    glb_newent_df <- myimport_data(
        url=glb_newdt_url, 
        comment="glb_newent_df", force_header=TRUE, print_diagn=TRUE)
} else {
    if (!glb_split_entity_newent_datasets) {
        stop("Not implemented yet") 
        glb_newent_df <- glb_entity_df[sample(1:nrow(glb_entity_df),
                                          max(2, nrow(glb_entity_df) / 1000)),]                    
    } else      if (glb_split_newdata_method == "condition") {
            glb_newent_df <- do.call("subset", 
                list(glb_entity_df, parse(text=glb_split_newdata_condition)))
            glb_entity_df <- do.call("subset", 
                list(glb_entity_df, parse(text=paste0("!(", 
                                                      glb_split_newdata_condition,
                                                      ")"))))
        } else if (glb_split_newdata_method == "sample") {
                require(caTools)
                
                set.seed(glb_split_sample.seed)
                split <- sample.split(glb_entity_df[, glb_rsp_var_raw], 
                                      SplitRatio=(1-glb_split_newdata_size_ratio))
                glb_newent_df <- glb_entity_df[!split, ] 
                glb_entity_df <- glb_entity_df[split ,]
        } else stop("glb_split_newdata_method should be %in% c('condition', 'sample')")   

    comment(glb_newent_df) <- "glb_newent_df"
    myprint_df(glb_newent_df)
    str(glb_newent_df)

    if (glb_split_entity_newent_datasets) {
        myprint_df(glb_entity_df)
        str(glb_entity_df)        
    }
}         
## Loading required package: caTools
##    age alzheimers arthritis cancer copd depression diabetes heart.failure
## 3   67          0         0      0    0          0        0             0
## 5   67          0         0      0    0          0        0             0
## 6   68          0         0      0    0          0        0             0
## 8   70          0         0      0    0          0        0             0
## 9   67          0         0      0    0          0        0             0
## 10  67          0         0      0    0          0        0             0
##    ihd kidney osteoporosis stroke reimbursement2008 bucket2008
## 3    0      0            0      0                 0          1
## 5    0      0            0      0                 0          1
## 6    0      0            0      0                 0          1
## 8    0      0            0      0                 0          1
## 9    0      0            0      0                 0          1
## 10   0      0            0      0                 0          1
##    reimbursement2009 bucket2009
## 3                  0          1
## 5                  0          1
## 6                  0          1
## 8                  0          1
## 9                  0          1
## 10                 0          1
##        age alzheimers arthritis cancer copd depression diabetes
## 43967   57          0         0      0    0          0        0
## 70246   70          0         0      0    0          0        0
## 165755  78          0         0      0    0          0        0
## 208131  73          0         1      1    0          0        0
## 319113  87          0         0      0    0          1        1
## 446073  72          1         0      1    0          1        1
##        heart.failure ihd kidney osteoporosis stroke reimbursement2008
## 43967              0   0      0            0      0                 0
## 70246              0   0      0            0      0                 0
## 165755             0   0      0            0      0               140
## 208131             0   0      0            1      0              5680
## 319113             0   1      0            0      0              2800
## 446073             1   1      0            1      1             16030
##        bucket2008 reimbursement2009 bucket2009
## 43967           1                 0          1
## 70246           1                 0          1
## 165755          1               720          1
## 208131          2              1250          1
## 319113          1              3330          2
## 446073          3             28000          4
##        age alzheimers arthritis cancer copd depression diabetes
## 457996  60          0         1      0    1          1        1
## 457998  87          0         0      0    1          1        1
## 458001  61          1         0      0    1          1        1
## 458002  90          1         0      0    1          1        1
## 458003  76          0         1      0    1          1        1
## 458005  80          1         0      0    1          1        1
##        heart.failure ihd kidney osteoporosis stroke reimbursement2008
## 457996             1   1      0            1      1             11720
## 457998             1   1      1            0      0             27750
## 458001             1   1      1            1      1             15960
## 458002             1   1      1            0      0             26870
## 458003             1   1      1            1      1             89140
## 458005             1   1      1            0      1             38320
##        bucket2008 reimbursement2009 bucket2009
## 457996          3            142960          5
## 457998          4            148600          5
## 458001          3            154000          5
## 458002          4            155010          5
## 458003          5            155810          5
## 458005          4            189930          5
## 'data.frame':    183202 obs. of  16 variables:
##  $ age              : int  67 67 68 70 67 67 56 48 99 68 ...
##  $ alzheimers       : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ arthritis        : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cancer           : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ copd             : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ depression       : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ diabetes         : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ heart.failure    : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ ihd              : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ kidney           : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ osteoporosis     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ stroke           : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ reimbursement2008: int  0 0 0 0 0 0 0 0 0 0 ...
##  $ bucket2008       : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ reimbursement2009: int  0 0 0 0 0 0 0 0 0 0 ...
##  $ bucket2009       : int  1 1 1 1 1 1 1 1 1 1 ...
##  - attr(*, "comment")= chr "glb_newent_df"
##    age alzheimers arthritis cancer copd depression diabetes heart.failure
## 1   85          0         0      0    0          0        0             0
## 2   59          0         0      0    0          0        0             0
## 4   52          0         0      0    0          0        0             0
## 7   75          0         0      0    0          0        0             0
## 11  89          0         0      0    0          0        0             0
## 13  74          0         0      0    0          0        0             0
##    ihd kidney osteoporosis stroke reimbursement2008 bucket2008
## 1    0      0            0      0                 0          1
## 2    0      0            0      0                 0          1
## 4    0      0            0      0                 0          1
## 7    0      0            0      0                 0          1
## 11   0      0            0      0                 0          1
## 13   0      0            0      0                 0          1
##    reimbursement2009 bucket2009
## 1                  0          1
## 2                  0          1
## 4                  0          1
## 7                  0          1
## 11                 0          1
## 13                 0          1
##        age alzheimers arthritis cancer copd depression diabetes
## 138659  69          0         0      0    0          0        0
## 168428  74          1         0      0    0          0        1
## 189703  81          0         0      0    0          0        1
## 225640  78          1         0      0    0          1        0
## 382169  77          1         0      0    1          1        1
## 397881  46          1         0      0    0          0        0
##        heart.failure ihd kidney osteoporosis stroke reimbursement2008
## 138659             0   0      0            0      0                 0
## 168428             0   0      0            1      0               720
## 189703             0   1      0            0      0               690
## 225640             0   0      0            1      0              1540
## 382169             1   1      1            1      1             16400
## 397881             1   1      1            0      0              3700
##        bucket2008 reimbursement2009 bucket2009
## 138659          1               380          1
## 168428          1               750          1
## 189703          1              1020          1
## 225640          1              1490          1
## 382169          3              6620          2
## 397881          2              8470          3
##        age alzheimers arthritis cancer copd depression diabetes
## 457991  76          0         0      0    1          1        1
## 457992  84          0         0      0    1          0        1
## 457997  73          0         0      0    1          1        1
## 457999  83          1         1      0    1          0        1
## 458000  56          0         1      0    1          1        1
## 458004  82          1         0      0    1          0        1
##        heart.failure ihd kidney osteoporosis stroke reimbursement2008
## 457991             1   1      1            1      0             53550
## 457992             1   1      1            0      0              8620
## 457997             1   1      1            1      0             53230
## 457999             1   1      1            1      1             62620
## 458000             1   1      1            1      0             62980
## 458004             1   1      1            1      1             20660
##        bucket2008 reimbursement2009 bucket2009
## 457991          4            131960          5
## 457992          3            133500          5
## 457997          4            147760          5
## 457999          5            148860          5
## 458000          5            151880          5
## 458004          4            158800          5
## 'data.frame':    274803 obs. of  16 variables:
##  $ age              : int  85 59 52 75 89 74 81 86 78 67 ...
##  $ alzheimers       : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ arthritis        : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cancer           : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ copd             : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ depression       : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ diabetes         : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ heart.failure    : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ ihd              : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ kidney           : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ osteoporosis     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ stroke           : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ reimbursement2008: int  0 0 0 0 0 0 0 0 0 0 ...
##  $ bucket2008       : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ reimbursement2009: int  0 0 0 0 0 0 0 0 0 0 ...
##  $ bucket2009       : int  1 1 1 1 1 1 1 1 1 1 ...
##  - attr(*, "comment")= chr "glb_entity_df"
if (!is.null(glb_max_obs)) {
    if (nrow(glb_entity_df) > glb_max_obs) {
        warning("glb_entity_df restricted to glb_max_obs: ", format(glb_max_obs, big.mark=","))
        org_entity_df <- glb_entity_df
        glb_entity_df <- org_entity_df[split <- 
            sample.split(org_entity_df[, glb_rsp_var_raw], SplitRatio=glb_max_obs), ]
        org_entity_df <- NULL
    }
    if (nrow(glb_newent_df) > glb_max_obs) {
        warning("glb_newent_df restricted to glb_max_obs: ", format(glb_max_obs, big.mark=","))        
        org_newent_df <- glb_newent_df
        glb_newent_df <- org_newent_df[split <- 
            sample.split(org_newent_df[, glb_rsp_var_raw], SplitRatio=glb_max_obs), ]
        org_newent_df <- NULL
    }    
}

glb_script_df <- rbind(glb_script_df,
                   data.frame(chunk_label="cleanse_data", 
                              chunk_step_major=max(glb_script_df$chunk_step_major)+1, 
                              chunk_step_minor=0,
                              elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))
##           chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed   import_data                1                0   0.003
## elapsed1 cleanse_data                2                0   7.036

Step 2: cleanse data

glb_script_df <- rbind(glb_script_df, 
                   data.frame(chunk_label="inspectORexplore.data", 
                              chunk_step_major=max(glb_script_df$chunk_step_major), 
                              chunk_step_minor=1,
                              elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))
##                    chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed1          cleanse_data                2                0   7.036
## elapsed2 inspectORexplore.data                2                1   7.066

Step 2.1: inspect/explore data

#print(str(glb_entity_df))
#View(glb_entity_df)

# List info gathered for various columns
# <col_name>:   <description>; <notes>

# Create new features that help diagnostics
#   Create factors of string variables
str_vars <- sapply(1:length(names(glb_entity_df)), 
    function(col) ifelse(class(glb_entity_df[, names(glb_entity_df)[col]]) == "character",
                         names(glb_entity_df)[col], ""))
if (length(str_vars <- setdiff(str_vars[str_vars != ""], 
                               glb_exclude_vars_as_features)) > 0) {
    warning("Creating factors of string variables:", paste0(str_vars, collapse=", "))
    glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, str_vars)
    for (var in str_vars) {
        glb_entity_df[, paste0(var, ".fctr")] <- factor(glb_entity_df[, var], 
                        as.factor(union(glb_entity_df[, var], glb_newent_df[, var])))
        glb_newent_df[, paste0(var, ".fctr")] <- factor(glb_newent_df[, var], 
                        as.factor(union(glb_entity_df[, var], glb_newent_df[, var])))
    }
}

#   Convert factors to dummy variables
#   Build splines   require(splines); bsBasis <- bs(training$age, df=3)

add_new_diag_feats <- function(obs_df, obs_twin_df) {
    require(plyr)
    
    obs_df <- mutate(obs_df,
#         <col_name>.NA=is.na(<col_name>),

#         <col_name>.fctr=factor(<col_name>, 
#                     as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))), 
#         <col_name>.fctr=relevel(factor(<col_name>, 
#                     as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
#                                   "<ref_val>"), 
#         <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")), 
#                               as.factor(c("R", "<ref_val>")),
#                               ref="<ref_val>"),

          # This doesn't work - use sapply instead
#         <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)), 
#         
#         Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
#         Year=year(Date.my),
#         Month=months(Date.my),
#         Weekday=weekdays(Date.my)

#         <col_name>.log=log(<col.name>),        
#         <col_name>=<table>[as.character(<col2_name>)],
#         <col_name>=as.numeric(<col2_name>),

        .rnorm=rnorm(n=nrow(obs_df))
                        )

    # If levels of a factor are different across obs_df & glb_newent_df; predict.glm fails  
    # Transformations not handled by mutate
#     obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df), 
#         function(row_ix) grep(obs_df[row_ix, "<col_name>"],
#                               levels(obs_df[row_ix, "<col_name>.fctr"])))
    
    print(summary(obs_df))
    print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
    return(obs_df)
}

glb_entity_df <- add_new_diag_feats(glb_entity_df, glb_newent_df)
## Loading required package: plyr
##       age           alzheimers       arthritis          cancer       
##  Min.   : 26.00   Min.   :0.0000   Min.   :0.0000   Min.   :0.00000  
##  1st Qu.: 67.00   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.00000  
##  Median : 73.00   Median :0.0000   Median :0.0000   Median :0.00000  
##  Mean   : 72.64   Mean   :0.1928   Mean   :0.1546   Mean   :0.06402  
##  3rd Qu.: 81.00   3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.:0.00000  
##  Max.   :100.00   Max.   :1.0000   Max.   :1.0000   Max.   :1.00000  
##       copd          depression        diabetes      heart.failure   
##  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
##  1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000  
##  Median :0.0000   Median :0.0000   Median :0.0000   Median :0.0000  
##  Mean   :0.1369   Mean   :0.2129   Mean   :0.3809   Mean   :0.2852  
##  3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.:1.0000   3rd Qu.:1.0000  
##  Max.   :1.0000   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
##       ihd             kidney        osteoporosis        stroke       
##  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.00000  
##  1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.00000  
##  Median :0.0000   Median :0.0000   Median :0.0000   Median :0.00000  
##  Mean   :0.4205   Mean   :0.1616   Mean   :0.1738   Mean   :0.04497  
##  3rd Qu.:1.0000   3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.:0.00000  
##  Max.   :1.0000   Max.   :1.0000   Max.   :1.0000   Max.   :1.00000  
##  reimbursement2008   bucket2008    reimbursement2009   bucket2009   
##  Min.   :     0    Min.   :1.000   Min.   :     0    Min.   :1.000  
##  1st Qu.:     0    1st Qu.:1.000   1st Qu.:   130    1st Qu.:1.000  
##  Median :   960    Median :1.000   Median :  1540    Median :1.000  
##  Mean   :  4016    Mean   :1.438   Mean   :  4280    Mean   :1.522  
##  3rd Qu.:  3110    3rd Qu.:2.000   3rd Qu.:  4220    3rd Qu.:2.000  
##  Max.   :194910    Max.   :5.000   Max.   :158800    Max.   :5.000  
##      .rnorm         
##  Min.   :-4.627678  
##  1st Qu.:-0.668424  
##  Median : 0.002423  
##  Mean   : 0.001867  
##  3rd Qu.: 0.675532  
##  Max.   : 4.385255  
##               age        alzheimers         arthritis            cancer 
##                 0                 0                 0                 0 
##              copd        depression          diabetes     heart.failure 
##                 0                 0                 0                 0 
##               ihd            kidney      osteoporosis            stroke 
##                 0                 0                 0                 0 
## reimbursement2008        bucket2008 reimbursement2009        bucket2009 
##                 0                 0                 0                 0 
##            .rnorm 
##                 0
glb_newent_df <- add_new_diag_feats(glb_newent_df, glb_entity_df)
##       age           alzheimers       arthritis          cancer       
##  Min.   : 26.00   Min.   :0.0000   Min.   :0.0000   Min.   :0.00000  
##  1st Qu.: 67.00   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.00000  
##  Median : 73.00   Median :0.0000   Median :0.0000   Median :0.00000  
##  Mean   : 72.61   Mean   :0.1914   Mean   :0.1539   Mean   :0.06424  
##  3rd Qu.: 81.00   3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.:0.00000  
##  Max.   :100.00   Max.   :1.0000   Max.   :1.0000   Max.   :1.00000  
##       copd          depression        diabetes      heart.failure   
##  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
##  1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000  
##  Median :0.0000   Median :0.0000   Median :0.0000   Median :0.0000  
##  Mean   :0.1348   Mean   :0.2133   Mean   :0.3798   Mean   :0.2841  
##  3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.:1.0000   3rd Qu.:1.0000  
##  Max.   :1.0000   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
##       ihd             kidney        osteoporosis        stroke       
##  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.00000  
##  1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.00000  
##  Median :0.0000   Median :0.0000   Median :0.0000   Median :0.00000  
##  Mean   :0.4195   Mean   :0.1605   Mean   :0.1743   Mean   :0.04453  
##  3rd Qu.:1.0000   3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.:0.00000  
##  Max.   :1.0000   Max.   :1.0000   Max.   :1.0000   Max.   :1.00000  
##  reimbursement2008   bucket2008    reimbursement2009   bucket2009   
##  Min.   :     0    Min.   :1.000   Min.   :     0    Min.   :1.000  
##  1st Qu.:     0    1st Qu.:1.000   1st Qu.:   120    1st Qu.:1.000  
##  Median :   950    Median :1.000   Median :  1530    Median :1.000  
##  Mean   :  3989    Mean   :1.436   Mean   :  4273    Mean   :1.522  
##  3rd Qu.:  3110    3rd Qu.:2.000   3rd Qu.:  4210    3rd Qu.:2.000  
##  Max.   :221640    Max.   :5.000   Max.   :189930    Max.   :5.000  
##      .rnorm         
##  Min.   :-4.224787  
##  1st Qu.:-0.673311  
##  Median : 0.001064  
##  Mean   : 0.002116  
##  3rd Qu.: 0.679841  
##  Max.   : 4.235888  
##               age        alzheimers         arthritis            cancer 
##                 0                 0                 0                 0 
##              copd        depression          diabetes     heart.failure 
##                 0                 0                 0                 0 
##               ihd            kidney      osteoporosis            stroke 
##                 0                 0                 0                 0 
## reimbursement2008        bucket2008 reimbursement2009        bucket2009 
##                 0                 0                 0                 0 
##            .rnorm 
##                 0
# Histogram of predictor in glb_entity_df & glb_newent_df
print(myplot_histogram(glb_entity_df, glb_rsp_var_raw))
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

if (glb_is_classification)
    print(table(glb_entity_df[, glb_rsp_var_raw]) / nrow(glb_entity_df))
## 
##           1           2           3           4           5 
## 0.671266325 0.190168957 0.089467728 0.043325582 0.005771407
print(myplot_histogram(glb_newent_df, glb_rsp_var_raw))
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

# Check for duplicates in glb_id_vars
if (length(glb_id_vars) > 0) {
    id_vars_dups_df <- subset(id_vars_df <- mycreate_tbl_df(
        rbind(glb_entity_df[, glb_id_vars, FALSE], glb_newent_df[, glb_id_vars, FALSE]),
            glb_id_vars), .freq > 1)
    if (nrow(id_vars_dups_df) > 0) {
        warning("Duplicates found in glb_id_vars data:", nrow(id_vars_dups_df))
        myprint_df(id_vars_dups_df)
    } else {
        # glb_id_vars are unique across obs in both glb_<>_df
        glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_id_vars)
    }
}

#pairs(subset(glb_entity_df, select=-c(col_symbol)))
# Check for glb_newent_df & glb_entity_df features range mismatches

# Other diagnostics:
# print(subset(glb_entity_df, <col1_name> == max(glb_entity_df$<col1_name>, na.rm=TRUE) & 
#                         <col2_name> <= mean(glb_entity_df$<col1_name>, na.rm=TRUE)))

# print(glb_entity_df[which.max(glb_entity_df$<col_name>),])

# print(<col_name>_freq_glb_entity_df <- mycreate_tbl_df(glb_entity_df, "<col_name>"))
# print(which.min(table(glb_entity_df$<col_name>)))
# print(which.max(table(glb_entity_df$<col_name>)))
# print(which.max(table(glb_entity_df$<col1_name>, glb_entity_df$<col2_name>)[, 2]))
# print(table(glb_entity_df$<col1_name>, glb_entity_df$<col2_name>))
# print(table(is.na(glb_entity_df$<col1_name>), glb_entity_df$<col2_name>))
# print(table(sign(glb_entity_df$<col1_name>), glb_entity_df$<col2_name>))
# print(mycreate_xtab(glb_entity_df, <col1_name>))
# print(mycreate_xtab(glb_entity_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_entity_df <- 
#   mycreate_xtab(glb_entity_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_entity_df[is.na(<col1_name>_<col2_name>_xtab_glb_entity_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_entity_df <- 
#   mutate(<col1_name>_<col2_name>_xtab_glb_entity_df, 
#             <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>))) 

# print(<col2_name>_min_entity_arr <- 
#    sort(tapply(glb_entity_df$<col1_name>, glb_entity_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <- 
#    sort(tapply(glb_entity_df$<col1_name>.NA, glb_entity_df$<col2_name>, mean, na.rm=TRUE)))

# Other plots:
# print(myplot_box(df=glb_entity_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_entity_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_entity_df, Symbol %in% c("KO", "PG")), 
#                   "Date.my", "StockPrice", facet_row_colnames="Symbol") + 
#     geom_vline(xintercept=as.numeric(as.Date("2003-03-01"))) +
#     geom_vline(xintercept=as.numeric(as.Date("1983-01-01")))        
#         )
# print(myplot_scatter(glb_entity_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_entity_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>"))

glb_script_df <- rbind(glb_script_df, 
    data.frame(chunk_label="manage_missing_data", 
        chunk_step_major=max(glb_script_df$chunk_step_major), 
        chunk_step_minor=glb_script_df[nrow(glb_script_df), "chunk_step_minor"]+1,
                              elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))
##                    chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed2 inspectORexplore.data                2                1   7.066
## elapsed3   manage_missing_data                2                2  16.418

Step 2.2: manage missing data

# print(sapply(names(glb_entity_df), function(col) sum(is.na(glb_entity_df[, col]))))
# print(sapply(names(glb_newent_df), function(col) sum(is.na(glb_newent_df[, col]))))
# glb_entity_df <- na.omit(glb_entity_df)
# glb_newent_df <- na.omit(glb_newent_df)
# df[is.na(df)] <- 0

# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function(entity_df, newent_df) {
    if (!glb_is_separate_newent_dataset) {
        # Combine entity & newent
        union_df <- rbind(mutate(entity_df, .src = "entity"),
                          mutate(newent_df, .src = "newent"))
        union_imputed_df <- union_df[, setdiff(setdiff(names(entity_df), 
                                                       glb_rsp_var), 
                                               glb_exclude_vars_as_features)]
        print(summary(union_imputed_df))
    
        require(mice)
        set.seed(glb_mice_complete.seed)
        union_imputed_df <- complete(mice(union_imputed_df))
        print(summary(union_imputed_df))
    
        union_df[, names(union_imputed_df)] <- union_imputed_df[, names(union_imputed_df)]
        print(summary(union_df))
#         union_df$.rownames <- rownames(union_df)
#         union_df <- orderBy(~.rownames, union_df)
#         
#         imp_entity_df <- myimport_data(
#             url="<imputed_trnng_url>", 
#             comment="imp_entity_df", force_header=TRUE, print_diagn=TRUE)
#         print(all.equal(subset(union_df, select=-c(.src, .rownames, .rnorm)), 
#                         imp_entity_df))
        
        # Partition again
        glb_entity_df <<- subset(union_df, .src == "entity", select=-c(.src, .rownames))
        comment(glb_entity_df) <- "entity_df"
        glb_newent_df <<- subset(union_df, .src == "newent", select=-c(.src, .rownames))
        comment(glb_newent_df) <- "newent_df"
        
        # Generate summaries
        print(summary(entity_df))
        print(sapply(names(entity_df), function(col) sum(is.na(entity_df[, col]))))
        print(summary(newent_df))
        print(sapply(names(newent_df), function(col) sum(is.na(newent_df[, col]))))
    
    } else stop("Not implemented yet")
}

if (glb_impute_na_data) {
    if ((sum(sapply(names(glb_entity_df), 
                    function(col) sum(is.na(glb_entity_df[, col])))) > 0) | 
        (sum(sapply(names(glb_newent_df), 
                    function(col) sum(is.na(glb_newent_df[, col])))) > 0))
        glb_impute_missing_data(glb_entity_df, glb_newent_df)
}    

glb_script_df <- rbind(glb_script_df, 
    data.frame(chunk_label="encode_retype_data", 
        chunk_step_major=max(glb_script_df$chunk_step_major), 
        chunk_step_minor=glb_script_df[nrow(glb_script_df), "chunk_step_minor"]+1,
                              elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))
##                  chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed3 manage_missing_data                2                2  16.418
## elapsed4  encode_retype_data                2                3  16.992

Step 2.3: encode/retype data

# map_<col_name>_df <- myimport_data(
#     url="<map_url>", 
#     comment="map_<col_name>_df", print_diagn=TRUE)
# map_<col_name>_df <- read.csv(paste0(getwd(), "/data/<file_name>.csv"), strip.white=TRUE)

# glb_entity_df <- mymap_codes(glb_entity_df, "<from_col_name>", "<to_col_name>", 
#     map_<to_col_name>_df, map_join_col_name="<map_join_col_name>", 
#                           map_tgt_col_name="<to_col_name>")
# glb_newent_df <- mymap_codes(glb_newent_df, "<from_col_name>", "<to_col_name>", 
#     map_<to_col_name>_df, map_join_col_name="<map_join_col_name>", 
#                           map_tgt_col_name="<to_col_name>")
                        
# glb_entity_df$<col_name>.fctr <- factor(glb_entity_df$<col_name>, 
#                     as.factor(union(glb_entity_df$<col_name>, glb_newent_df$<col_name>)))
# glb_newent_df$<col_name>.fctr <- factor(glb_newent_df$<col_name>, 
#                     as.factor(union(glb_entity_df$<col_name>, glb_newent_df$<col_name>)))

if (!is.null(glb_map_rsp_raw_to_var)) {
    glb_entity_df[, glb_rsp_var] <- 
        glb_map_rsp_raw_to_var(glb_entity_df[, glb_rsp_var_raw])
    mycheck_map_results(mapd_df=glb_entity_df, 
                        from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
        
    glb_newent_df[, glb_rsp_var] <- 
        glb_map_rsp_raw_to_var(glb_newent_df[, glb_rsp_var_raw])
    mycheck_map_results(mapd_df=glb_newent_df, 
                        from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)    

    glb_entity_df[, "bucket2008.fctr"] <- 
        glb_map_rsp_raw_to_var(glb_entity_df[, "bucket2008"])
    mycheck_map_results(mapd_df=glb_entity_df, 
                        from_col_name="bucket2008", to_col_name="bucket2008.fctr")
        
    glb_newent_df[, "bucket2008.fctr"] <- 
        glb_map_rsp_raw_to_var(glb_newent_df[, "bucket2008"])
    mycheck_map_results(mapd_df=glb_newent_df, 
                        from_col_name="bucket2008", to_col_name="bucket2008.fctr")    

}
## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk
##   bucket2009 bucket2009.fctr     .n
## 1          1              B1 184466
## 2          2              B2  52259
## 3          3              B3  24586
## 4          4              B4  11906
## 5          5              B5   1586

##   bucket2009 bucket2009.fctr     .n
## 1          1              B1 122978
## 2          2              B2  34840
## 3          3              B3  16390
## 4          4              B4   7937
## 5          5              B5   1057

##   bucket2008 bucket2008.fctr     .n
## 1          1              B1 204077
## 2          2              B2  37902
## 3          3              B3  18442
## 4          4              B4  12062
## 5          5              B5   2320

##   bucket2008 bucket2008.fctr     .n
## 1          1              B1 136125
## 2          2              B2  25271
## 3          3              B3  12405
## 4          4              B4   7852
## 5          5              B5   1549

glb_script_df <- rbind(glb_script_df, 
                   data.frame(chunk_label="extract_features", 
                              chunk_step_major=max(glb_script_df$chunk_step_major)+1, 
                              chunk_step_minor=0,
                              elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))
##                 chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed4 encode_retype_data                2                3  16.992
## elapsed5   extract_features                3                0  26.774

Step 3: extract features

# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_entity_df$<col_name>), -2, na.pad=TRUE)
# glb_entity_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newent_df$<col_name>), -2, na.pad=TRUE)
# glb_newent_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# 
# glb_newent_df[1, "<col_name>.lag.2"] <- glb_entity_df[nrow(glb_entity_df) - 1, 
#                                                    "<col_name>"]
# glb_newent_df[2, "<col_name>.lag.2"] <- glb_entity_df[nrow(glb_entity_df), 
#                                                    "<col_name>"]
                                                   
# glb_entity_df <- mutate(glb_entity_df,
#     <new_col_name>=
#                     )

# glb_newent_df <- mutate(glb_newent_df,
#     <new_col_name>=
#                     )

# print(summary(glb_entity_df))
# print(summary(glb_newent_df))

# print(sapply(names(glb_entity_df), function(col) sum(is.na(glb_entity_df[, col]))))
# print(sapply(names(glb_newent_df), function(col) sum(is.na(glb_newent_df[, col]))))

# print(myplot_scatter(glb_entity_df, "<col1_name>", "<col2_name>", smooth=TRUE))

replay.petrisim(pn=glb_analytics_pn, 
    replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "data.training.all","data.new")), flip_coord=TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0

glb_script_df <- rbind(glb_script_df, 
                   data.frame(chunk_label="select_features", 
                              chunk_step_major=max(glb_script_df$chunk_step_major)+1, 
                              chunk_step_minor=0,
                              elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))
##               chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed5 extract_features                3                0  26.774
## elapsed6  select_features                4                0  28.472

Step 4: select features

print(glb_feats_df <- myselect_features(entity_df=glb_entity_df, 
                       exclude_vars_as_features=glb_exclude_vars_as_features, 
                       rsp_var=glb_rsp_var))
##                                  id         cor.y exclude.as.feat
## bucket2009               bucket2009  1.0000000000               1
## reimbursement2009 reimbursement2009  0.8581631864               1
## bucket2008               bucket2008  0.4518935763               0
## bucket2008.fctr     bucket2008.fctr  0.4518935763               1
## ihd                             ihd  0.3905905884               0
## diabetes                   diabetes  0.3904719536               0
## reimbursement2008 reimbursement2008  0.3756473063               0
## kidney                       kidney  0.3683780944               0
## heart.failure         heart.failure  0.3647689526               0
## copd                           copd  0.3108325355               0
## depression               depression  0.2835366153               0
## alzheimers               alzheimers  0.2741643394               0
## arthritis                 arthritis  0.2717113526               0
## cancer                       cancer  0.2100892954               0
## osteoporosis           osteoporosis  0.2076745377               0
## stroke                       stroke  0.1846626746               0
## age                             age  0.0495694151               0
## .rnorm                       .rnorm -0.0007970401               0
##                      cor.y.abs
## bucket2009        1.0000000000
## reimbursement2009 0.8581631864
## bucket2008        0.4518935763
## bucket2008.fctr   0.4518935763
## ihd               0.3905905884
## diabetes          0.3904719536
## reimbursement2008 0.3756473063
## kidney            0.3683780944
## heart.failure     0.3647689526
## copd              0.3108325355
## depression        0.2835366153
## alzheimers        0.2741643394
## arthritis         0.2717113526
## cancer            0.2100892954
## osteoporosis      0.2076745377
## stroke            0.1846626746
## age               0.0495694151
## .rnorm            0.0007970401
glb_script_df <- rbind(glb_script_df, 
    data.frame(chunk_label="remove_correlated_features", 
        chunk_step_major=max(glb_script_df$chunk_step_major),
        chunk_step_minor=glb_script_df[nrow(glb_script_df), "chunk_step_minor"]+1,
                              elapsed=(proc.time() - glb_script_tm)["elapsed"]))        
print(tail(glb_script_df, 2))
##                         chunk_label chunk_step_major chunk_step_minor
## elapsed6            select_features                4                0
## elapsed7 remove_correlated_features                4                1
##          elapsed
## elapsed6  28.472
## elapsed7  29.537

Step 4.1: remove correlated features

print(glb_feats_df <- orderBy(~-cor.y, 
          myfind_cor_features(feats_df=glb_feats_df, entity_df=glb_entity_df, 
                                rsp_var=glb_rsp_var)))
## Loading required package: reshape2
##                     bucket2008          ihd     diabetes reimbursement2008
## bucket2008         1.000000000  0.457745311  0.441695494       0.847360100
## ihd                0.457745311  1.000000000  0.514681463       0.362167974
## diabetes           0.441695494  0.514681463  1.000000000       0.349826333
## reimbursement2008  0.847360100  0.362167974  0.349826333       1.000000000
## kidney             0.556443060  0.379348673  0.410508010       0.482661680
## heart.failure      0.483790416  0.466853617  0.447713361       0.397862155
## copd               0.479911725  0.341479503  0.337436848       0.411894455
## depression         0.342770130  0.323337325  0.347401660       0.275910696
## alzheimers         0.375421748  0.326534346  0.341021686       0.313974193
## arthritis          0.330250474  0.311822976  0.323405716       0.250483735
## cancer             0.295879956  0.192569561  0.182463340       0.244638721
## osteoporosis       0.234597479  0.265948840  0.273431836       0.185149970
## stroke             0.319686629  0.193566819  0.191335431       0.296068271
## age                0.065957565  0.072806906  0.067414459       0.049189684
## .rnorm            -0.005471367 -0.002117255 -0.001223323      -0.003838492
##                         kidney heart.failure         copd   depression
## bucket2008         0.556443060    0.48379042  0.479911725  0.342770130
## ihd                0.379348673    0.46685362  0.341479503  0.323337325
## diabetes           0.410508010    0.44771336  0.337436848  0.347401660
## reimbursement2008  0.482661680    0.39786215  0.411894455  0.275910696
## kidney             1.000000000    0.41853590  0.362145211  0.274868719
## heart.failure      0.418535903    1.00000000  0.376619956  0.303108016
## copd               0.362145211    0.37661996  1.000000000  0.262091958
## depression         0.274868719    0.30310802  0.262091958  1.000000000
## alzheimers         0.304331472    0.33163762  0.282316055  0.289283692
## arthritis          0.250350192    0.26577236  0.218711881  0.231412945
## cancer             0.187306219    0.17399979  0.173576229  0.120859944
## osteoporosis       0.192706719    0.22170825  0.184441404  0.193200212
## stroke             0.229542821    0.22049646  0.206759478  0.155228820
## age                0.056523917    0.06758002  0.044475985  0.013183881
## .rnorm            -0.006698442   -0.00413497 -0.003209717 -0.004546531
##                     alzheimers    arthritis       cancer  osteoporosis
## bucket2008         0.375421748  0.330250474  0.295879956  0.2345974786
## ihd                0.326534346  0.311822976  0.192569561  0.2659488405
## diabetes           0.341021686  0.323405716  0.182463340  0.2734318364
## reimbursement2008  0.313974193  0.250483735  0.244638721  0.1851499698
## kidney             0.304331472  0.250350192  0.187306219  0.1927067194
## heart.failure      0.331637621  0.265772357  0.173999790  0.2217082477
## copd               0.282316055  0.218711881  0.173576229  0.1844414035
## depression         0.289283692  0.231412945  0.120859944  0.1932002120
## alzheimers         1.000000000  0.205787238  0.136229360  0.1898304044
## arthritis          0.205787238  1.000000000  0.116224739  0.2188952357
## cancer             0.136229360  0.116224739  1.000000000  0.1145554395
## osteoporosis       0.189830404  0.218895236  0.114555439  1.0000000000
## stroke             0.217944953  0.117518638  0.100858117  0.1000348636
## age                0.044509085  0.042003908  0.037706744  0.0400590867
## .rnorm            -0.002242699 -0.003328174 -0.001080007 -0.0007172342
##                         stroke          age        .rnorm
## bucket2008         0.319686629 0.0659575650 -0.0054713667
## ihd                0.193566819 0.0728069059 -0.0021172555
## diabetes           0.191335431 0.0674144586 -0.0012233233
## reimbursement2008  0.296068271 0.0491896842 -0.0038384924
## kidney             0.229542821 0.0565239171 -0.0066984421
## heart.failure      0.220496456 0.0675800164 -0.0041349700
## copd               0.206759478 0.0444759850 -0.0032097166
## depression         0.155228820 0.0131838813 -0.0045465313
## alzheimers         0.217944953 0.0445090852 -0.0022426985
## arthritis          0.117518638 0.0420039076 -0.0033281745
## cancer             0.100858117 0.0377067437 -0.0010800072
## osteoporosis       0.100034864 0.0400590867 -0.0007172342
## stroke             1.000000000 0.0365102875 -0.0034834136
## age                0.036510287 1.0000000000  0.0007606978
## .rnorm            -0.003483414 0.0007606978  1.0000000000
##                    bucket2008         ihd    diabetes reimbursement2008
## bucket2008        0.000000000 0.457745311 0.441695494       0.847360100
## ihd               0.457745311 0.000000000 0.514681463       0.362167974
## diabetes          0.441695494 0.514681463 0.000000000       0.349826333
## reimbursement2008 0.847360100 0.362167974 0.349826333       0.000000000
## kidney            0.556443060 0.379348673 0.410508010       0.482661680
## heart.failure     0.483790416 0.466853617 0.447713361       0.397862155
## copd              0.479911725 0.341479503 0.337436848       0.411894455
## depression        0.342770130 0.323337325 0.347401660       0.275910696
## alzheimers        0.375421748 0.326534346 0.341021686       0.313974193
## arthritis         0.330250474 0.311822976 0.323405716       0.250483735
## cancer            0.295879956 0.192569561 0.182463340       0.244638721
## osteoporosis      0.234597479 0.265948840 0.273431836       0.185149970
## stroke            0.319686629 0.193566819 0.191335431       0.296068271
## age               0.065957565 0.072806906 0.067414459       0.049189684
## .rnorm            0.005471367 0.002117255 0.001223323       0.003838492
##                        kidney heart.failure        copd  depression
## bucket2008        0.556443060    0.48379042 0.479911725 0.342770130
## ihd               0.379348673    0.46685362 0.341479503 0.323337325
## diabetes          0.410508010    0.44771336 0.337436848 0.347401660
## reimbursement2008 0.482661680    0.39786215 0.411894455 0.275910696
## kidney            0.000000000    0.41853590 0.362145211 0.274868719
## heart.failure     0.418535903    0.00000000 0.376619956 0.303108016
## copd              0.362145211    0.37661996 0.000000000 0.262091958
## depression        0.274868719    0.30310802 0.262091958 0.000000000
## alzheimers        0.304331472    0.33163762 0.282316055 0.289283692
## arthritis         0.250350192    0.26577236 0.218711881 0.231412945
## cancer            0.187306219    0.17399979 0.173576229 0.120859944
## osteoporosis      0.192706719    0.22170825 0.184441404 0.193200212
## stroke            0.229542821    0.22049646 0.206759478 0.155228820
## age               0.056523917    0.06758002 0.044475985 0.013183881
## .rnorm            0.006698442    0.00413497 0.003209717 0.004546531
##                    alzheimers   arthritis      cancer osteoporosis
## bucket2008        0.375421748 0.330250474 0.295879956 0.2345974786
## ihd               0.326534346 0.311822976 0.192569561 0.2659488405
## diabetes          0.341021686 0.323405716 0.182463340 0.2734318364
## reimbursement2008 0.313974193 0.250483735 0.244638721 0.1851499698
## kidney            0.304331472 0.250350192 0.187306219 0.1927067194
## heart.failure     0.331637621 0.265772357 0.173999790 0.2217082477
## copd              0.282316055 0.218711881 0.173576229 0.1844414035
## depression        0.289283692 0.231412945 0.120859944 0.1932002120
## alzheimers        0.000000000 0.205787238 0.136229360 0.1898304044
## arthritis         0.205787238 0.000000000 0.116224739 0.2188952357
## cancer            0.136229360 0.116224739 0.000000000 0.1145554395
## osteoporosis      0.189830404 0.218895236 0.114555439 0.0000000000
## stroke            0.217944953 0.117518638 0.100858117 0.1000348636
## age               0.044509085 0.042003908 0.037706744 0.0400590867
## .rnorm            0.002242699 0.003328174 0.001080007 0.0007172342
##                        stroke          age       .rnorm
## bucket2008        0.319686629 0.0659575650 0.0054713667
## ihd               0.193566819 0.0728069059 0.0021172555
## diabetes          0.191335431 0.0674144586 0.0012233233
## reimbursement2008 0.296068271 0.0491896842 0.0038384924
## kidney            0.229542821 0.0565239171 0.0066984421
## heart.failure     0.220496456 0.0675800164 0.0041349700
## copd              0.206759478 0.0444759850 0.0032097166
## depression        0.155228820 0.0131838813 0.0045465313
## alzheimers        0.217944953 0.0445090852 0.0022426985
## arthritis         0.117518638 0.0420039076 0.0033281745
## cancer            0.100858117 0.0377067437 0.0010800072
## osteoporosis      0.100034864 0.0400590867 0.0007172342
## stroke            0.000000000 0.0365102875 0.0034834136
## age               0.036510287 0.0000000000 0.0007606978
## .rnorm            0.003483414 0.0007606978 0.0000000000
## [1] "cor(bucket2008, reimbursement2008)=0.8474"

## [1] "cor(bucket2009.fctr, bucket2008)=0.4519"
## [1] "cor(bucket2009.fctr, reimbursement2008)=0.3756"
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_entity_df, : Identified reimbursement2008 as highly correlated with
## other features

## [1] "checking correlations for features:"
##  [1] "bucket2008"    "ihd"           "diabetes"      "kidney"       
##  [5] "heart.failure" "copd"          "depression"    "alzheimers"   
##  [9] "arthritis"     "cancer"        "osteoporosis"  "stroke"       
## [13] "age"           ".rnorm"       
##                 bucket2008          ihd     diabetes       kidney
## bucket2008     1.000000000  0.457745311  0.441695494  0.556443060
## ihd            0.457745311  1.000000000  0.514681463  0.379348673
## diabetes       0.441695494  0.514681463  1.000000000  0.410508010
## kidney         0.556443060  0.379348673  0.410508010  1.000000000
## heart.failure  0.483790416  0.466853617  0.447713361  0.418535903
## copd           0.479911725  0.341479503  0.337436848  0.362145211
## depression     0.342770130  0.323337325  0.347401660  0.274868719
## alzheimers     0.375421748  0.326534346  0.341021686  0.304331472
## arthritis      0.330250474  0.311822976  0.323405716  0.250350192
## cancer         0.295879956  0.192569561  0.182463340  0.187306219
## osteoporosis   0.234597479  0.265948840  0.273431836  0.192706719
## stroke         0.319686629  0.193566819  0.191335431  0.229542821
## age            0.065957565  0.072806906  0.067414459  0.056523917
## .rnorm        -0.005471367 -0.002117255 -0.001223323 -0.006698442
##               heart.failure         copd   depression   alzheimers
## bucket2008       0.48379042  0.479911725  0.342770130  0.375421748
## ihd              0.46685362  0.341479503  0.323337325  0.326534346
## diabetes         0.44771336  0.337436848  0.347401660  0.341021686
## kidney           0.41853590  0.362145211  0.274868719  0.304331472
## heart.failure    1.00000000  0.376619956  0.303108016  0.331637621
## copd             0.37661996  1.000000000  0.262091958  0.282316055
## depression       0.30310802  0.262091958  1.000000000  0.289283692
## alzheimers       0.33163762  0.282316055  0.289283692  1.000000000
## arthritis        0.26577236  0.218711881  0.231412945  0.205787238
## cancer           0.17399979  0.173576229  0.120859944  0.136229360
## osteoporosis     0.22170825  0.184441404  0.193200212  0.189830404
## stroke           0.22049646  0.206759478  0.155228820  0.217944953
## age              0.06758002  0.044475985  0.013183881  0.044509085
## .rnorm          -0.00413497 -0.003209717 -0.004546531 -0.002242699
##                  arthritis       cancer  osteoporosis       stroke
## bucket2008     0.330250474  0.295879956  0.2345974786  0.319686629
## ihd            0.311822976  0.192569561  0.2659488405  0.193566819
## diabetes       0.323405716  0.182463340  0.2734318364  0.191335431
## kidney         0.250350192  0.187306219  0.1927067194  0.229542821
## heart.failure  0.265772357  0.173999790  0.2217082477  0.220496456
## copd           0.218711881  0.173576229  0.1844414035  0.206759478
## depression     0.231412945  0.120859944  0.1932002120  0.155228820
## alzheimers     0.205787238  0.136229360  0.1898304044  0.217944953
## arthritis      1.000000000  0.116224739  0.2188952357  0.117518638
## cancer         0.116224739  1.000000000  0.1145554395  0.100858117
## osteoporosis   0.218895236  0.114555439  1.0000000000  0.100034864
## stroke         0.117518638  0.100858117  0.1000348636  1.000000000
## age            0.042003908  0.037706744  0.0400590867  0.036510287
## .rnorm        -0.003328174 -0.001080007 -0.0007172342 -0.003483414
##                        age        .rnorm
## bucket2008    0.0659575650 -0.0054713667
## ihd           0.0728069059 -0.0021172555
## diabetes      0.0674144586 -0.0012233233
## kidney        0.0565239171 -0.0066984421
## heart.failure 0.0675800164 -0.0041349700
## copd          0.0444759850 -0.0032097166
## depression    0.0131838813 -0.0045465313
## alzheimers    0.0445090852 -0.0022426985
## arthritis     0.0420039076 -0.0033281745
## cancer        0.0377067437 -0.0010800072
## osteoporosis  0.0400590867 -0.0007172342
## stroke        0.0365102875 -0.0034834136
## age           1.0000000000  0.0007606978
## .rnorm        0.0007606978  1.0000000000
##                bucket2008         ihd    diabetes      kidney
## bucket2008    0.000000000 0.457745311 0.441695494 0.556443060
## ihd           0.457745311 0.000000000 0.514681463 0.379348673
## diabetes      0.441695494 0.514681463 0.000000000 0.410508010
## kidney        0.556443060 0.379348673 0.410508010 0.000000000
## heart.failure 0.483790416 0.466853617 0.447713361 0.418535903
## copd          0.479911725 0.341479503 0.337436848 0.362145211
## depression    0.342770130 0.323337325 0.347401660 0.274868719
## alzheimers    0.375421748 0.326534346 0.341021686 0.304331472
## arthritis     0.330250474 0.311822976 0.323405716 0.250350192
## cancer        0.295879956 0.192569561 0.182463340 0.187306219
## osteoporosis  0.234597479 0.265948840 0.273431836 0.192706719
## stroke        0.319686629 0.193566819 0.191335431 0.229542821
## age           0.065957565 0.072806906 0.067414459 0.056523917
## .rnorm        0.005471367 0.002117255 0.001223323 0.006698442
##               heart.failure        copd  depression  alzheimers
## bucket2008       0.48379042 0.479911725 0.342770130 0.375421748
## ihd              0.46685362 0.341479503 0.323337325 0.326534346
## diabetes         0.44771336 0.337436848 0.347401660 0.341021686
## kidney           0.41853590 0.362145211 0.274868719 0.304331472
## heart.failure    0.00000000 0.376619956 0.303108016 0.331637621
## copd             0.37661996 0.000000000 0.262091958 0.282316055
## depression       0.30310802 0.262091958 0.000000000 0.289283692
## alzheimers       0.33163762 0.282316055 0.289283692 0.000000000
## arthritis        0.26577236 0.218711881 0.231412945 0.205787238
## cancer           0.17399979 0.173576229 0.120859944 0.136229360
## osteoporosis     0.22170825 0.184441404 0.193200212 0.189830404
## stroke           0.22049646 0.206759478 0.155228820 0.217944953
## age              0.06758002 0.044475985 0.013183881 0.044509085
## .rnorm           0.00413497 0.003209717 0.004546531 0.002242699
##                 arthritis      cancer osteoporosis      stroke
## bucket2008    0.330250474 0.295879956 0.2345974786 0.319686629
## ihd           0.311822976 0.192569561 0.2659488405 0.193566819
## diabetes      0.323405716 0.182463340 0.2734318364 0.191335431
## kidney        0.250350192 0.187306219 0.1927067194 0.229542821
## heart.failure 0.265772357 0.173999790 0.2217082477 0.220496456
## copd          0.218711881 0.173576229 0.1844414035 0.206759478
## depression    0.231412945 0.120859944 0.1932002120 0.155228820
## alzheimers    0.205787238 0.136229360 0.1898304044 0.217944953
## arthritis     0.000000000 0.116224739 0.2188952357 0.117518638
## cancer        0.116224739 0.000000000 0.1145554395 0.100858117
## osteoporosis  0.218895236 0.114555439 0.0000000000 0.100034864
## stroke        0.117518638 0.100858117 0.1000348636 0.000000000
## age           0.042003908 0.037706744 0.0400590867 0.036510287
## .rnorm        0.003328174 0.001080007 0.0007172342 0.003483414
##                        age       .rnorm
## bucket2008    0.0659575650 0.0054713667
## ihd           0.0728069059 0.0021172555
## diabetes      0.0674144586 0.0012233233
## kidney        0.0565239171 0.0066984421
## heart.failure 0.0675800164 0.0041349700
## copd          0.0444759850 0.0032097166
## depression    0.0131838813 0.0045465313
## alzheimers    0.0445090852 0.0022426985
## arthritis     0.0420039076 0.0033281745
## cancer        0.0377067437 0.0010800072
## osteoporosis  0.0400590867 0.0007172342
## stroke        0.0365102875 0.0034834136
## age           0.0000000000 0.0007606978
## .rnorm        0.0007606978 0.0000000000
##                                  id         cor.y exclude.as.feat
## bucket2009               bucket2009  1.0000000000               1
## reimbursement2009 reimbursement2009  0.8581631864               1
## bucket2008               bucket2008  0.4518935763               0
## bucket2008.fctr     bucket2008.fctr  0.4518935763               1
## ihd                             ihd  0.3905905884               0
## diabetes                   diabetes  0.3904719536               0
## reimbursement2008 reimbursement2008  0.3756473063               0
## kidney                       kidney  0.3683780944               0
## heart.failure         heart.failure  0.3647689526               0
## copd                           copd  0.3108325355               0
## depression               depression  0.2835366153               0
## alzheimers               alzheimers  0.2741643394               0
## arthritis                 arthritis  0.2717113526               0
## cancer                       cancer  0.2100892954               0
## osteoporosis           osteoporosis  0.2076745377               0
## stroke                       stroke  0.1846626746               0
## age                             age  0.0495694151               0
## .rnorm                       .rnorm -0.0007970401               0
##                      cor.y.abs cor.low
## bucket2009        1.0000000000       0
## reimbursement2009 0.8581631864       0
## bucket2008        0.4518935763       1
## bucket2008.fctr   0.4518935763       0
## ihd               0.3905905884       1
## diabetes          0.3904719536       1
## reimbursement2008 0.3756473063       0
## kidney            0.3683780944       1
## heart.failure     0.3647689526       1
## copd              0.3108325355       1
## depression        0.2835366153       1
## alzheimers        0.2741643394       1
## arthritis         0.2717113526       1
## cancer            0.2100892954       1
## osteoporosis      0.2076745377       1
## stroke            0.1846626746       1
## age               0.0495694151       1
## .rnorm            0.0007970401       1
glb_script_df <- rbind(glb_script_df, 
                   data.frame(chunk_label="fit.models", 
                              chunk_step_major=max(glb_script_df$chunk_step_major)+1, 
                              chunk_step_minor=0,
                              elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))
##                         chunk_label chunk_step_major chunk_step_minor
## elapsed7 remove_correlated_features                4                1
## elapsed8                 fit.models                5                0
##          elapsed
## elapsed7  29.537
## elapsed8  71.848

Step 5: fit models

max_cor_y_x_var <- subset(glb_feats_df, cor.low == 1)[1, "id"]
if (!is.null(glb_Baseline_mdl_var)) {
    if ((max_cor_y_x_var != glb_Baseline_mdl_var) & 
        (glb_feats_df[max_cor_y_x_var, "cor.y.abs"] > 
         glb_feats_df[glb_Baseline_mdl_var, "cor.y.abs"]))
        stop(max_cor_y_x_var, " has a lower correlation with ", glb_rsp_var, 
             " than the Baseline var: ", glb_Baseline_mdl_var)
}

#   Regression:
if (glb_is_regression) {
    #   Linear:
    myfit_mdl_fn <- myfit_mdl_lm
}    

#   Classification:
if (glb_is_classification) myfit_mdl_fn <- myfit_mdl_classification 
glb_is_binomial <- (length(unique(glb_entity_df[, glb_rsp_var])) <= 2)

# Any models that have tuning parameters has "better" results with cross-validation
#   & "different" results for different outcome metrics

# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
#     lm_mdl <- lm(reformulate(glb_Baseline_mdl_var, 
#                             response="bucket2009"), data=glb_entity_df)
#     print(summary(lm_mdl))
#     plot(lm_mdl, ask=FALSE)
#     ret_lst <- myfit_mdl_fn(model_id="Baseline", 
#                             model_method=ifelse(glb_is_regression, "lm", 
#                                         ifelse(glb_is_binomial, "glm", "rpart")),
#                             indep_vars_vctr=glb_Baseline_mdl_var,
#                             rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
#                             fit_df=glb_entity_df, OOB_df=glb_newent_df,
#                             n_cv_folds=0, tune_models_df=NULL,
#                             model_loss_mtrx=glb_model_metric_terms,
#                             model_summaryFunction=glb_model_metric_smmry,
#                             model_metric=glb_model_metric,
#                             model_metric_maximize=glb_model_metric_maximize)
    ret_lst <- myfit_mdl_fn(model_id="Baseline", model_method="mybaseln_classfr",
                            indep_vars_vctr=glb_Baseline_mdl_var,
                            rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
                            fit_df=glb_entity_df, OOB_df=glb_newent_df)
}
## Loading required package: caret
## Loading required package: lattice
## 
## Attaching package: 'caret'
## 
## The following object is masked from 'package:survival':
## 
##     cluster
## [1] "fitting model: Baseline.mybaseln_classfr"
## [1] "    indep_vars: bucket2008.fctr, .rnorm"
## Warning in if (model_method == "rf") methodControl <- "oob": the condition
## has length > 1 and only the first element will be used
## Fitting parameter = none on full training set
## [1] "in Baseline.Classifier$fit"
## [1] "class(x):"
## [1] "matrix"
## [1] "dimnames(x)[[2]]:"
## [1] "bucket2008.fctrB2" "bucket2008.fctrB3" "bucket2008.fctrB4"
## [4] "bucket2008.fctrB5" ".rnorm"           
## [1] "length(x):"
## [1] 1374015
## [1] "head(x):"
##    bucket2008.fctrB2 bucket2008.fctrB3 bucket2008.fctrB4 bucket2008.fctrB5
## 1                  0                 0                 0                 0
## 2                  0                 0                 0                 0
## 4                  0                 0                 0                 0
## 7                  0                 0                 0                 0
## 11                 0                 0                 0                 0
## 13                 0                 0                 0                 0
##        .rnorm
## 1  -0.9248001
## 2   0.1533902
## 4  -0.9917587
## 7  -0.5160354
## 11 -1.0376029
## 13 -0.3801495
## [1] "class(y):"
## [1] "factor"
## [1] "length(y):"
## [1] 274803
## [1] "head(y):"
##  1  2  4  7 11 13 
## B1 B1 B1 B1 B1 B1 
## Levels: B1 B2 B3 B4 B5
##             Length Class      Mode     
## x_names     4      -none-     character
## x_vals      5      -none-     character
## xNames      5      -none-     character
## problemType 1      -none-     character
## tuneValue   1      data.frame list     
## obsLevels   5      -none-     character
## [1] "in Baseline.Classifier$predict"
## [1] "class(newdata):"
## [1] "matrix"
## [1] "head(newdata):"
##    bucket2008.fctrB2 bucket2008.fctrB3 bucket2008.fctrB4 bucket2008.fctrB5
## 1                  0                 0                 0                 0
## 2                  0                 0                 0                 0
## 4                  0                 0                 0                 0
## 7                  0                 0                 0                 0
## 11                 0                 0                 0                 0
## 13                 0                 0                 0                 0
##        .rnorm
## 1  -0.9248001
## 2   0.1533902
## 4  -0.9917587
## 7  -0.5160354
## 11 -1.0376029
## 13 -0.3801495
## [1] "x_names: "
## [1] "bucket2008.fctrB2" "bucket2008.fctrB3" "bucket2008.fctrB4"
## [4] "bucket2008.fctrB5"
## [1] "x_vals: "
## [1] "B1" "B2" "B3" "B4" "B5"
## [1] "length(y):"
## [1] 274803
## [1] "head(y):"
## [1] B1 B1 B1 B1 B1 B1
## Levels: B1 B2 B3 B4 B5
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 164967  11747   5214   2275    263
##        B2  24001  16172   6877   4367    842
##        B3  10679   6848   4004   2552    503
##        B4   4020   2835   2081   2399    571
##        B5    410    300    266    469    141
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.829729e-01             NA   6.812294e-01   6.847125e-01   6.712663e-01 
## AccuracyPValue  McnemarPValue 
##   1.601032e-39   0.000000e+00 
## [1] "in Baseline.Classifier$predict"
## [1] "class(newdata):"
## [1] "matrix"
## [1] "head(newdata):"
##    bucket2008.fctrB2 bucket2008.fctrB3 bucket2008.fctrB4 bucket2008.fctrB5
## 3                  0                 0                 0                 0
## 5                  0                 0                 0                 0
## 6                  0                 0                 0                 0
## 8                  0                 0                 0                 0
## 9                  0                 0                 0                 0
## 10                 0                 0                 0                 0
##           .rnorm
## 3   0.7183313438
## 5   0.0008759006
## 6  -1.4428971189
## 8  -1.9680342619
## 9  -3.0026827087
## 10 -1.1723168041
## [1] "x_names: "
## [1] "bucket2008.fctrB2" "bucket2008.fctrB3" "bucket2008.fctrB4"
## [4] "bucket2008.fctrB5"
## [1] "x_vals: "
## [1] "B1" "B2" "B3" "B4" "B5"
## [1] "length(y):"
## [1] 183202
## [1] "head(y):"
## [1] B1 B1 B1 B1 B1 B1
## Levels: B1 B2 B3 B4 B5
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 110138   7787   3427   1452    174
##        B2  16000  10721   4629   2931    559
##        B3   7006   4629   2774   1621    360
##        B4   2688   1943   1415   1539    352
##        B5    293    191    160    309    104
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.838135e-01             NA   6.816786e-01   6.859425e-01   6.712700e-01 
## AccuracyPValue  McnemarPValue 
##   9.943570e-31   0.000000e+00 
##                    model_id     model_method                   feats
## 1 Baseline.mybaseln_classfr mybaseln_classfr bucket2008.fctr, .rnorm
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1               0                      0.626                 0.006
##   max.Accuracy.fit max.AccuracyLower.fit max.AccuracyUpper.fit
## 1        0.6829729             0.6812294             0.6847125
##   max.Kappa.fit max.Accuracy.OOB max.AccuracyLower.OOB
## 1            NA        0.6838135             0.6816786
##   max.AccuracyUpper.OOB max.Kappa.OOB min.SSE.fit
## 1             0.6859425            NA           0
# Most Frequent Outcome "MFO" model: mean(y) for regression
#   Not using caret's nullModel since model stats not avl
#   Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl_fn(model_id="MFO", model_method="myMFO_classfr",
                        indep_vars_vctr=".rnorm",
                        rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
                        fit_df=glb_entity_df, OOB_df=glb_newent_df)
## [1] "fitting model: MFO.myMFO_classfr"
## [1] "    indep_vars: .rnorm"
## Warning in if (model_method == "rf") methodControl <- "oob": the condition
## has length > 1 and only the first element will be used
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] B1 B2 B3 B4 B5
## Levels: B1 B2 B3 B4 B5
## [1] "unique.prob:"
## y
##          B1          B2          B3          B4          B5 
## 0.671266325 0.190168957 0.089467728 0.043325582 0.005771407 
## [1] "MFO.val:"
## [1] "B1"
##             Length Class      Mode     
## unique.vals 5      factor     numeric  
## unique.prob 5      -none-     numeric  
## MFO.val     1      -none-     character
## xNames      1      -none-     character
## problemType 1      -none-     character
## tuneValue   1      data.frame list     
## obsLevels   5      -none-     character
## [1] "in MFO.Classifier$predict"
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 184466      0      0      0      0
##        B2  52259      0      0      0      0
##        B3  24586      0      0      0      0
##        B4  11906      0      0      0      0
##        B5   1586      0      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.6712663             NA      0.6695064      0.6730227      0.6712663 
## AccuracyPValue  McnemarPValue 
##      0.5009025            NaN 
## [1] "in MFO.Classifier$predict"
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 122978      0      0      0      0
##        B2  34840      0      0      0      0
##        B3  16390      0      0      0      0
##        B4   7937      0      0      0      0
##        B5   1057      0      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.6712700             NA      0.6691135      0.6734210      0.6712700 
## AccuracyPValue  McnemarPValue 
##      0.5011054            NaN 
##            model_id  model_method  feats max.nTuningRuns
## 1 MFO.myMFO_classfr myMFO_classfr .rnorm               0
##   min.elapsedtime.everything min.elapsedtime.final max.Accuracy.fit
## 1                      0.515                 0.038        0.6712663
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.6695064             0.6730227            NA
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1          0.67127             0.6691135              0.673421
##   max.Kappa.OOB min.SSE.fit
## 1            NA           0
# "random" model - only for classification; none needed for regression since it is same as MFO
ret_lst <- myfit_mdl_fn(model_id="Random", model_method="myrandom_classfr",
                        indep_vars_vctr=".rnorm",
                        rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
                        fit_df=glb_entity_df, OOB_df=glb_newent_df)
## [1] "fitting model: Random.myrandom_classfr"
## [1] "    indep_vars: .rnorm"
## Warning in if (model_method == "rf") methodControl <- "oob": the condition
## has length > 1 and only the first element will be used
## Fitting parameter = none on full training set
##             Length Class      Mode     
## unique.vals 5      factor     numeric  
## unique.prob 5      table      numeric  
## xNames      1      -none-     character
## problemType 1      -none-     character
## tuneValue   1      data.frame list     
## obsLevels   5      -none-     character
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 123841  35132  16435   7984   1074
##        B2  35132   9946   4671   2224    286
##        B3  16483   4699   2202   1081    121
##        B4   8003   2259   1085    503     56
##        B5   1056    325    127     68     10
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.4967267             NA      0.4948556      0.4985980      0.6712663 
## AccuracyPValue  McnemarPValue 
##      1.0000000      0.9272432
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference    B1    B2    B3    B4    B5
##        B1 82207 23808 10990  5253   720
##        B2 23379  6626  3141  1506   188
##        B3 11037  3119  1460   683    91
##        B4  5329  1497   705   364    42
##        B5   707   204    97    43     6
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.4948800             NA      0.4925879      0.4971722      0.6712700 
## AccuracyPValue  McnemarPValue 
##      1.0000000      0.8174342 
##                  model_id     model_method  feats max.nTuningRuns
## 1 Random.myrandom_classfr myrandom_classfr .rnorm               0
##   min.elapsedtime.everything min.elapsedtime.final max.Accuracy.fit
## 1                      0.375                 0.035        0.4967267
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.4948556              0.498598            NA
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1          0.49488             0.4925879             0.4971722
##   max.Kappa.OOB min.SSE.fit
## 1            NA           0
# Max.cor.Y
ret_lst <- myfit_mdl_fn(model_id="Max.cor.Y.cv.0", 
                        model_method=ifelse(glb_is_regression, "lm", 
                                        ifelse(glb_is_binomial, "glm", "rpart")),
                        indep_vars_vctr=max_cor_y_x_var,
                        rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
                        fit_df=glb_entity_df, OOB_df=glb_newent_df)
## [1] "fitting model: Max.cor.Y.cv.0.rpart"
## [1] "    indep_vars: bucket2008"
## Loading required package: rpart
## Fitting cp = 0.097 on full training set
## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7, 
##     cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, 
##     surrogatestyle = 0, maxdepth = 30, xval = 0))
##   n= 274803 
## 
##           CP nsplit rel error
## 1 0.09695916      0         1
## 
## Node number 1: 274803 observations
##   predicted class=B1  expected loss=0.3287337  P(node) =1
##     class counts: 184466 52259 24586 11906  1586
##    probabilities: 0.671 0.190 0.089 0.043 0.006 
## 
## n= 274803 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 274803 90337 B1 (0.67 0.19 0.089 0.043 0.0058) *
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 184466      0      0      0      0
##        B2  52259      0      0      0      0
##        B3  24586      0      0      0      0
##        B4  11906      0      0      0      0
##        B5   1586      0      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.6712663             NA      0.6695064      0.6730227      0.6712663 
## AccuracyPValue  McnemarPValue 
##      0.5009025            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow

##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 122978      0      0      0      0
##        B2  34840      0      0      0      0
##        B3  16390      0      0      0      0
##        B4   7937      0      0      0      0
##        B5   1057      0      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.6712700             NA      0.6691135      0.6734210      0.6712700 
## AccuracyPValue  McnemarPValue 
##      0.5011054            NaN 
##               model_id model_method      feats max.nTuningRuns
## 1 Max.cor.Y.cv.0.rpart        rpart bucket2008               0
##   min.elapsedtime.everything min.elapsedtime.final max.Accuracy.fit
## 1                      4.811                  3.43        0.6712663
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.6695064             0.6730227            NA
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1          0.67127             0.6691135              0.673421
##   max.Kappa.OOB min.SSE.fit
## 1            NA           0
ret_lst <- myfit_mdl_fn(model_id="Max.cor.Y.cv.G", 
                        model_method=ifelse(glb_is_regression, "lm", 
                                        ifelse(glb_is_binomial, "glm", "rpart")),
                        indep_vars_vctr=max_cor_y_x_var,
                        rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
                        fit_df=glb_entity_df, OOB_df=glb_newent_df,
                        n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
## [1] "fitting model: Max.cor.Y.cv.G.rpart"
## [1] "    indep_vars: bucket2008"
## + Fold1: cp=0 
## - Fold1: cp=0 
## + Fold2: cp=0 
## - Fold2: cp=0 
## + Fold3: cp=0 
## - Fold3: cp=0 
## + Fold4: cp=0 
## - Fold4: cp=0 
## + Fold5: cp=0 
## - Fold5: cp=0 
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0485 on full training set

## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7, 
##     cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, 
##     surrogatestyle = 0, maxdepth = 30, xval = 0))
##   n= 274803 
## 
##           CP nsplit rel error
## 1 0.09695916      0 1.0000000
## 2 0.00000000      1 0.9030408
## 
## Variable importance
## bucket2008 
##        100 
## 
## Node number 1: 274803 observations,    complexity param=0.09695916
##   predicted class=B1  expected loss=0.3287337  P(node) =1
##     class counts: 184466 52259 24586 11906  1586
##    probabilities: 0.671 0.190 0.089 0.043 0.006 
##   left son=2 (204077 obs) right son=3 (70726 obs)
##   Primary splits:
##       bucket2008 < 1.5 to the left,  improve=20624.7, (0 missing)
## 
## Node number 2: 204077 observations
##   predicted class=B1  expected loss=0.1916434  P(node) =0.7426302
##     class counts: 164967 24001 10679  4020   410
##    probabilities: 0.808 0.118 0.052 0.020 0.002 
## 
## Node number 3: 70726 observations
##   predicted class=B2  expected loss=0.6004581  P(node) =0.2573698
##     class counts: 19499 28258 13907  7886  1176
##    probabilities: 0.276 0.400 0.197 0.112 0.017 
## 
## n= 274803 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 274803 90337 B1 (0.67 0.19 0.089 0.043 0.0058)  
##   2) bucket2008< 1.5 204077 39110 B1 (0.81 0.12 0.052 0.02 0.002) *
##   3) bucket2008>=1.5 70726 42468 B2 (0.28 0.4 0.2 0.11 0.017) *
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 164967  19499      0      0      0
##        B2  24001  28258      0      0      0
##        B3  10679  13907      0      0      0
##        B4   4020   7886      0      0      0
##        B5    410   1176      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.031401e-01             NA   7.014279e-01   7.048479e-01   6.712663e-01 
## AccuracyPValue  McnemarPValue 
##  3.041172e-282            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow

##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 110138  12840      0      0      0
##        B2  16000  18840      0      0      0
##        B3   7006   9384      0      0      0
##        B4   2688   5249      0      0      0
##        B5    293    764      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.040207e-01             NA   7.019245e-01   7.061105e-01   6.712700e-01 
## AccuracyPValue  McnemarPValue 
##  1.813355e-199            NaN 
##               model_id model_method      feats max.nTuningRuns
## 1 Max.cor.Y.cv.G.rpart        rpart bucket2008               3
##   min.elapsedtime.everything min.elapsedtime.final max.Accuracy.fit
## 1                     20.801                 3.333        0.7031401
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.7014279             0.7048479     0.3440289
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.7040207             0.7019245             0.7061105
##   max.Kappa.OOB min.SSE.fit max.AccuracySD.fit max.KappaSD.fit
## 1            NA           0        0.001665788     0.004508795
# Interactions.High.cor.Y
if (nrow(int_feats_df <- subset(glb_feats_df, (cor.low == 0) & 
                                              (exclude.as.feat == 0))) > 0) {
    # Only glm handles interaction terms (checked that rpart does not)
    #   This does not work - why ???
#     indep_vars_vctr <- ifelse(glb_is_binomial, 
#         c(max_cor_y_x_var, paste(max_cor_y_x_var, 
#                         subset(glb_feats_df, is.na(cor.low))[, "id"], sep=":")),
#         union(max_cor_y_x_var, subset(glb_feats_df, is.na(cor.low))[, "id"]))
    if (glb_is_regression | glb_is_binomial) {
        indep_vars_vctr <- 
            c(max_cor_y_x_var, paste(max_cor_y_x_var, int_feats_df[, "id"], sep=":"))       
    } else { indep_vars_vctr <- union(max_cor_y_x_var, int_feats_df[, "id"]) }
    
    ret_lst <- myfit_mdl_fn(model_id="Interact.High.cor.y", 
                            model_method=ifelse(glb_is_regression, "lm", 
                                        ifelse(glb_is_binomial, "glm", "rpart")),
                            indep_vars_vctr,
                            glb_rsp_var, glb_rsp_var_out,
                            fit_df=glb_entity_df, OOB_df=glb_newent_df,
                            n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)                        
}    
## [1] "fitting model: Interact.High.cor.y.rpart"
## [1] "    indep_vars: bucket2008, reimbursement2008"
## + Fold1: cp=4.428e-05 
## - Fold1: cp=4.428e-05 
## + Fold2: cp=4.428e-05 
## - Fold2: cp=4.428e-05 
## + Fold3: cp=4.428e-05 
## - Fold3: cp=4.428e-05 
## + Fold4: cp=4.428e-05 
## - Fold4: cp=4.428e-05 
## + Fold5: cp=4.428e-05 
## - Fold5: cp=4.428e-05 
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 4.67e-05 on full training set

## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7, 
##     cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, 
##     surrogatestyle = 0, maxdepth = 30, xval = 0))
##   n= 274803 
## 
##             CP nsplit rel error
## 1 4.908841e-02      0 1.0000000
## 2 4.665072e-05      2 0.9018232
## 
## Variable importance
## reimbursement2008        bucket2008 
##                60                40 
## 
## Node number 1: 274803 observations,    complexity param=0.04908841
##   predicted class=B1  expected loss=0.3287337  P(node) =1
##     class counts: 184466 52259 24586 11906  1586
##    probabilities: 0.671 0.190 0.089 0.043 0.006 
##   left son=2 (165987 obs) right son=3 (108816 obs)
##   Primary splits:
##       reimbursement2008 < 1565 to the left,  improve=24395.14, (0 missing)
##       bucket2008        < 1.5  to the left,  improve=20624.70, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5  to the left,  agree=0.861, adj=0.65, (0 split)
## 
## Node number 2: 165987 observations
##   predicted class=B1  expected loss=0.1261424  P(node) =0.6040218
##     class counts: 145049 12284  6102  2315   237
##    probabilities: 0.874 0.074 0.037 0.014 0.001 
## 
## Node number 3: 108816 observations,    complexity param=0.04908841
##   predicted class=B2  expected loss=0.6326367  P(node) =0.3959782
##     class counts: 39417 39975 18484  9591  1349
##    probabilities: 0.362 0.367 0.170 0.088 0.012 
##   left son=6 (39298 obs) right son=7 (69518 obs)
##   Primary splits:
##       reimbursement2008 < 3065 to the left,  improve=2010.308, (0 missing)
##       bucket2008        < 1.5  to the left,  improve=1980.977, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5  to the left,  agree=0.989, adj=0.969, (0 split)
## 
## Node number 6: 39298 observations
##   predicted class=B1  expected loss=0.4797445  P(node) =0.1430043
##     class counts: 20445 12134  4756  1782   181
##    probabilities: 0.520 0.309 0.121 0.045 0.005 
## 
## Node number 7: 69518 observations
##   predicted class=B2  expected loss=0.5995138  P(node) =0.2529739
##     class counts: 18972 27841 13728  7809  1168
##    probabilities: 0.273 0.400 0.197 0.112 0.017 
## 
## n= 274803 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 274803 90337 B1 (0.67 0.19 0.089 0.043 0.0058)  
##   2) reimbursement2008< 1565 165987 20938 B1 (0.87 0.074 0.037 0.014 0.0014) *
##   3) reimbursement2008>=1565 108816 68841 B2 (0.36 0.37 0.17 0.088 0.012)  
##     6) reimbursement2008< 3065 39298 18853 B1 (0.52 0.31 0.12 0.045 0.0046) *
##     7) reimbursement2008>=3065 69518 41677 B2 (0.27 0.4 0.2 0.11 0.017) *
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 165494  18972      0      0      0
##        B2  24418  27841      0      0      0
##        B3  10858  13728      0      0      0
##        B4   4097   7809      0      0      0
##        B5    418   1168      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.035404e-01             NA   7.018289e-01   7.052475e-01   6.712663e-01 
## AccuracyPValue  McnemarPValue 
##  2.207353e-289            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow

##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 110452  12526      0      0      0
##        B2  16322  18518      0      0      0
##        B3   7105   9285      0      0      0
##        B4   2740   5197      0      0      0
##        B5    299    758      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.039770e-01             NA   7.018807e-01   7.060669e-01   6.712700e-01 
## AccuracyPValue  McnemarPValue 
##  6.148674e-199            NaN 
##                    model_id model_method                         feats
## 1 Interact.High.cor.y.rpart        rpart bucket2008, reimbursement2008
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1               3                     31.301                  5.05
##   max.Accuracy.fit max.AccuracyLower.fit max.AccuracyUpper.fit
## 1          0.70298             0.7018289             0.7052475
##   max.Kappa.fit max.Accuracy.OOB max.AccuracyLower.OOB
## 1     0.3368565         0.703977             0.7018807
##   max.AccuracyUpper.OOB max.Kappa.OOB min.SSE.fit max.AccuracySD.fit
## 1             0.7060669            NA           0        0.001690032
##   max.KappaSD.fit
## 1     0.006361514
# Low.cor.X
ret_lst <- myfit_mdl_fn(model_id="Low.cor.X", 
                        model_method=ifelse(glb_is_regression, "lm", 
                                        ifelse(glb_is_binomial, "glm", "rpart")),
                        indep_vars_vctr=subset(glb_feats_df, cor.low == 1)[, "id"],
                        glb_rsp_var, glb_rsp_var_out,
                        fit_df=glb_entity_df, OOB_df=glb_newent_df,
                        n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
## [1] "fitting model: Low.cor.X.rpart"
## [1] "    indep_vars: bucket2008, ihd, diabetes, kidney, heart.failure, copd, depression, alzheimers, arthritis, cancer, osteoporosis, stroke, age"
## + Fold1: cp=0.004317 
## - Fold1: cp=0.004317 
## + Fold2: cp=0.004317 
## - Fold2: cp=0.004317 
## + Fold3: cp=0.004317 
## - Fold3: cp=0.004317 
## + Fold4: cp=0.004317 
## - Fold4: cp=0.004317 
## + Fold5: cp=0.004317 
## - Fold5: cp=0.004317 
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.00432 on full training set
## Warning in myfit_mdl_fn(model_id = "Low.cor.X", model_method =
## ifelse(glb_is_regression, : model's bestTune found at an extreme of
## tuneGrid for parameter: cp

## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7, 
##     cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, 
##     surrogatestyle = 0, maxdepth = 30, xval = 0))
##   n= 274803 
## 
##            CP nsplit rel error
## 1 0.096959164      0 1.0000000
## 2 0.015010461      1 0.9030408
## 3 0.004317168      2 0.8880304
## 
## Variable importance
##    bucket2008        kidney          copd heart.failure     arthritis 
##            49            15            12             9             7 
##    alzheimers      diabetes 
##             6             2 
## 
## Node number 1: 274803 observations,    complexity param=0.09695916
##   predicted class=B1  expected loss=0.3287337  P(node) =1
##     class counts: 184466 52259 24586 11906  1586
##    probabilities: 0.671 0.190 0.089 0.043 0.006 
##   left son=2 (204077 obs) right son=3 (70726 obs)
##   Primary splits:
##       bucket2008    < 1.5 to the left,  improve=20624.70, (0 missing)
##       ihd           < 0.5 to the left,  improve=16291.74, (0 missing)
##       diabetes      < 0.5 to the left,  improve=16041.26, (0 missing)
##       heart.failure < 0.5 to the left,  improve=12498.16, (0 missing)
##       kidney        < 0.5 to the left,  improve=10965.93, (0 missing)
##   Surrogate splits:
##       kidney        < 0.5 to the left,  agree=0.823, adj=0.311, (0 split)
##       copd          < 0.5 to the left,  agree=0.806, adj=0.246, (0 split)
##       heart.failure < 0.5 to the left,  agree=0.791, adj=0.187, (0 split)
##       arthritis     < 0.5 to the left,  agree=0.782, adj=0.153, (0 split)
##       alzheimers    < 0.5 to the left,  agree=0.774, adj=0.120, (0 split)
## 
## Node number 2: 204077 observations
##   predicted class=B1  expected loss=0.1916434  P(node) =0.7426302
##     class counts: 164967 24001 10679  4020   410
##    probabilities: 0.808 0.118 0.052 0.020 0.002 
## 
## Node number 3: 70726 observations,    complexity param=0.01501046
##   predicted class=B2  expected loss=0.6004581  P(node) =0.2573698
##     class counts: 19499 28258 13907  7886  1176
##    probabilities: 0.276 0.400 0.197 0.112 0.017 
##   left son=6 (16128 obs) right son=7 (54598 obs)
##   Primary splits:
##       diabetes      < 0.5 to the left,  improve=662.2867, (0 missing)
##       kidney        < 0.5 to the left,  improve=620.6834, (0 missing)
##       arthritis     < 0.5 to the left,  improve=517.1168, (0 missing)
##       ihd           < 0.5 to the left,  improve=443.6660, (0 missing)
##       heart.failure < 0.5 to the left,  improve=397.1855, (0 missing)
## 
## Node number 6: 16128 observations
##   predicted class=B1  expected loss=0.5680184  P(node) =0.05868932
##     class counts:  6967  5611  2420  1017   113
##    probabilities: 0.432 0.348 0.150 0.063 0.007 
## 
## Node number 7: 54598 observations
##   predicted class=B2  expected loss=0.5852046  P(node) =0.1986805
##     class counts: 12532 22647 11487  6869  1063
##    probabilities: 0.230 0.415 0.210 0.126 0.019 
## 
## n= 274803 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 274803 90337 B1 (0.67 0.19 0.089 0.043 0.0058)  
##   2) bucket2008< 1.5 204077 39110 B1 (0.81 0.12 0.052 0.02 0.002) *
##   3) bucket2008>=1.5 70726 42468 B2 (0.28 0.4 0.2 0.11 0.017)  
##     6) diabetes< 0.5 16128  9161 B1 (0.43 0.35 0.15 0.063 0.007) *
##     7) diabetes>=0.5 54598 31951 B2 (0.23 0.41 0.21 0.13 0.019) *
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 171934  12532      0      0      0
##        B2  29612  22647      0      0      0
##        B3  13099  11487      0      0      0
##        B4   5037   6869      0      0      0
##        B5    523   1063      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.7080745             NA      0.7063706      0.7097740      0.6712663 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow

##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 114716   8262      0      0      0
##        B2  19896  14944      0      0      0
##        B3   8672   7718      0      0      0
##        B4   3366   4571      0      0      0
##        B5    379    678      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.077434e-01             NA   7.056548e-01   7.098254e-01   6.712700e-01 
## AccuracyPValue  McnemarPValue 
##  2.343725e-247            NaN 
##          model_id model_method
## 1 Low.cor.X.rpart        rpart
##                                                                                                                          feats
## 1 bucket2008, ihd, diabetes, kidney, heart.failure, copd, depression, alzheimers, arthritis, cancer, osteoporosis, stroke, age
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1               3                    101.182                13.937
##   max.Accuracy.fit max.AccuracyLower.fit max.AccuracyUpper.fit
## 1        0.7086058             0.7063706              0.709774
##   max.Kappa.fit max.Accuracy.OOB max.AccuracyLower.OOB
## 1     0.3161651        0.7077434             0.7056548
##   max.AccuracyUpper.OOB max.Kappa.OOB min.SSE.fit max.AccuracySD.fit
## 1             0.7098254            NA           0       0.0009742811
##   max.KappaSD.fit
## 1     0.004868262
# User specified
for (method in glb_models_method_vctr) {
    print(sprintf("iterating over method:%s", method))

    # All X that is not user excluded
    indep_vars_vctr <- setdiff(names(glb_entity_df), 
        union(glb_rsp_var, glb_exclude_vars_as_features))
    
    # easier to exclude features
#     indep_vars_vctr <- setdiff(names(glb_entity_df), 
#         union(union(glb_rsp_var, glb_exclude_vars_as_features), 
#               c("<feat1_name>", "<feat2_name>")))
    
    # easier to include features
#     indep_vars_vctr <- c("<feat1_name>", "<feat2_name>")

    # User specified bivariate models
#     indep_vars_vctr_lst <- list()
#     for (feat in setdiff(names(glb_entity_df), 
#                          union(glb_rsp_var, glb_exclude_vars_as_features)))
#         indep_vars_vctr_lst[["feat"]] <- feat

    # User specified combinatorial models
#     indep_vars_vctr_lst <- list()
#     combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"), 
#                           <num_feats_to_choose>)
#     for (combn_ix in 1:ncol(combn_mtrx))
#         #print(combn_mtrx[, combn_ix])
#         indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]

#     glb_sel_mdl <- glb_sel_wlm_mdl <- ret_lst[["model"]]
#     rpart_sel_wlm_mdl <- rpart(reformulate(indep_vars_vctr, response=glb_rsp_var), 
#                                data=glb_entity_df, method="class", 
#                                control=rpart.control(cp=glb_sel_wlm_mdl$bestTune$cp),
#                            parms=list(loss=glb_model_metric_terms))
#     print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
# 
    model_id_pfx <- "All.X";
    ret_lst <- myfit_mdl_fn(model_id=paste0(model_id_pfx, ".lser.no.cp.opt"), model_method=method,
                            indep_vars_vctr=indep_vars_vctr,
                            rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
                            fit_df=glb_entity_df, OOB_df=glb_newent_df,
                            n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
    if (method == "rpart")
    ret_lst <- myfit_mdl_fn(model_id=paste0(model_id_pfx, ".lser.no.cp.4015"), model_method=method,
                            indep_vars_vctr=indep_vars_vctr,
                            rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
                            fit_df=glb_entity_df, OOB_df=glb_newent_df,
                            n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
    
    # rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
    #   only for OOB in trainControl ?
    ret_lst <- myfit_mdl_fn(model_id=paste0(model_id_pfx, ".lser.ys.cp.opt"), model_method=method,
                            indep_vars_vctr=indep_vars_vctr,
                            rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
                            fit_df=glb_entity_df, OOB_df=glb_newent_df,
                            n_cv_folds=glb_n_cv_folds, tune_models_df=NULL,
                            model_loss_mtrx=glb_model_metric_terms,
                            model_summaryFunction=glb_model_metric_smmry,
                            model_metric=glb_model_metric,
                            model_metric_maximize=glb_model_metric_maximize)
    if (method == "rpart")
    ret_lst <- myfit_mdl_fn(model_id=paste0(model_id_pfx, ".lser.ys.cp.4015"), model_method=method,
                            indep_vars_vctr=indep_vars_vctr,
                            rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
                            fit_df=glb_entity_df, OOB_df=glb_newent_df,
                            n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df,
                            model_loss_mtrx=glb_model_metric_terms,
                            model_summaryFunction=glb_model_metric_smmry,
                            model_metric=glb_model_metric,
                            model_metric_maximize=glb_model_metric_maximize)
}
## [1] "iterating over method:rpart"
## [1] "fitting model: All.X.lser.no.cp.opt.rpart"
## [1] "    indep_vars: age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008"
## + Fold1: cp=0.00435 
## - Fold1: cp=0.00435 
## + Fold2: cp=0.00435 
## - Fold2: cp=0.00435 
## + Fold3: cp=0.00435 
## - Fold3: cp=0.00435 
## + Fold4: cp=0.00435 
## - Fold4: cp=0.00435 
## + Fold5: cp=0.00435 
## - Fold5: cp=0.00435 
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.00435 on full training set
## Warning in myfit_mdl_fn(model_id = paste0(model_id_pfx,
## ".lser.no.cp.opt"), : model's bestTune found at an extreme of tuneGrid for
## parameter: cp

## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7, 
##     cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, 
##     surrogatestyle = 0, maxdepth = 30, xval = 0))
##   n= 274803 
## 
##            CP nsplit rel error
## 1 0.049088413      0 1.0000000
## 2 0.013958843      2 0.9018232
## 3 0.004350377      3 0.8878643
## 
## Variable importance
## reimbursement2008        bucket2008          diabetes               ihd 
##                31                21                14                14 
##     heart.failure            kidney 
##                11                 9 
## 
## Node number 1: 274803 observations,    complexity param=0.04908841
##   predicted class=B1  expected loss=0.3287337  P(node) =1
##     class counts: 184466 52259 24586 11906  1586
##    probabilities: 0.671 0.190 0.089 0.043 0.006 
##   left son=2 (165987 obs) right son=3 (108816 obs)
##   Primary splits:
##       reimbursement2008 < 1565 to the left,  improve=24395.14, (0 missing)
##       bucket2008        < 1.5  to the left,  improve=20624.70, (0 missing)
##       ihd               < 0.5  to the left,  improve=16291.74, (0 missing)
##       diabetes          < 0.5  to the left,  improve=16041.26, (0 missing)
##       heart.failure     < 0.5  to the left,  improve=12498.16, (0 missing)
##   Surrogate splits:
##       bucket2008    < 1.5  to the left,  agree=0.861, adj=0.650, (0 split)
##       ihd           < 0.5  to the left,  agree=0.792, adj=0.474, (0 split)
##       diabetes      < 0.5  to the left,  agree=0.785, adj=0.456, (0 split)
##       heart.failure < 0.5  to the left,  agree=0.762, adj=0.399, (0 split)
##       kidney        < 0.5  to the left,  agree=0.731, adj=0.321, (0 split)
## 
## Node number 2: 165987 observations
##   predicted class=B1  expected loss=0.1261424  P(node) =0.6040218
##     class counts: 145049 12284  6102  2315   237
##    probabilities: 0.874 0.074 0.037 0.014 0.001 
## 
## Node number 3: 108816 observations,    complexity param=0.04908841
##   predicted class=B2  expected loss=0.6326367  P(node) =0.3959782
##     class counts: 39417 39975 18484  9591  1349
##    probabilities: 0.362 0.367 0.170 0.088 0.012 
##   left son=6 (39298 obs) right son=7 (69518 obs)
##   Primary splits:
##       reimbursement2008 < 3065 to the left,  improve=2010.3080, (0 missing)
##       bucket2008        < 1.5  to the left,  improve=1980.9770, (0 missing)
##       kidney            < 0.5  to the left,  improve=1416.9220, (0 missing)
##       diabetes          < 0.5  to the left,  improve=1236.1460, (0 missing)
##       heart.failure     < 0.5  to the left,  improve= 976.9427, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5  to the left,  agree=0.989, adj=0.969, (0 split)
##       ihd        < 0.5  to the left,  agree=0.659, adj=0.056, (0 split)
##       diabetes   < 0.5  to the left,  agree=0.641, adj=0.006, (0 split)
## 
## Node number 6: 39298 observations
##   predicted class=B1  expected loss=0.4797445  P(node) =0.1430043
##     class counts: 20445 12134  4756  1782   181
##    probabilities: 0.520 0.309 0.121 0.045 0.005 
## 
## Node number 7: 69518 observations,    complexity param=0.01395884
##   predicted class=B2  expected loss=0.5995138  P(node) =0.2529739
##     class counts: 18972 27841 13728  7809  1168
##    probabilities: 0.273 0.400 0.197 0.112 0.017 
##   left son=14 (15717 obs) right son=15 (53801 obs)
##   Primary splits:
##       diabetes      < 0.5  to the left,  improve=646.4740, (0 missing)
##       kidney        < 0.5  to the left,  improve=604.0313, (0 missing)
##       arthritis     < 0.5  to the left,  improve=501.1263, (0 missing)
##       ihd           < 0.5  to the left,  improve=427.9009, (0 missing)
##       heart.failure < 0.5  to the left,  improve=380.0080, (0 missing)
## 
## Node number 14: 15717 observations
##   predicted class=B1  expected loss=0.5704651  P(node) =0.0571937
##     class counts:  6751  5490  2365   999   112
##    probabilities: 0.430 0.349 0.150 0.064 0.007 
## 
## Node number 15: 53801 observations
##   predicted class=B2  expected loss=0.5845616  P(node) =0.1957802
##     class counts: 12221 22351 11363  6810  1056
##    probabilities: 0.227 0.415 0.211 0.127 0.020 
## 
## n= 274803 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
##  1) root 274803 90337 B1 (0.67 0.19 0.089 0.043 0.0058)  
##    2) reimbursement2008< 1565 165987 20938 B1 (0.87 0.074 0.037 0.014 0.0014) *
##    3) reimbursement2008>=1565 108816 68841 B2 (0.36 0.37 0.17 0.088 0.012)  
##      6) reimbursement2008< 3065 39298 18853 B1 (0.52 0.31 0.12 0.045 0.0046) *
##      7) reimbursement2008>=3065 69518 41677 B2 (0.27 0.4 0.2 0.11 0.017)  
##       14) diabetes< 0.5 15717  8966 B1 (0.43 0.35 0.15 0.064 0.0071) *
##       15) diabetes>=0.5 53801 31450 B2 (0.23 0.42 0.21 0.13 0.02) *
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 172245  12221      0      0      0
##        B2  29908  22351      0      0      0
##        B3  13223  11363      0      0      0
##        B4   5096   6810      0      0      0
##        B5    530   1056      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.7081291             NA      0.7064253      0.7098285      0.6712663 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow

##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 114902   8076      0      0      0
##        B2  20130  14710      0      0      0
##        B3   8749   7641      0      0      0
##        B4   3409   4528      0      0      0
##        B5    382    675      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.074814e-01             NA   7.053922e-01   7.095639e-01   6.712700e-01 
## AccuracyPValue  McnemarPValue 
##  8.205962e-244            NaN 
##                     model_id model_method
## 1 All.X.lser.no.cp.opt.rpart        rpart
##                                                                                                                                             feats
## 1 age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1               3                     107.61                15.544
##   max.Accuracy.fit max.AccuracyLower.fit max.AccuracyUpper.fit
## 1        0.7086604             0.7064253             0.7098285
##   max.Kappa.fit max.Accuracy.OOB max.AccuracyLower.OOB
## 1     0.3140169        0.7074814             0.7053922
##   max.AccuracyUpper.OOB max.Kappa.OOB min.SSE.fit max.AccuracySD.fit
## 1             0.7095639            NA           0        0.001014054
##   max.KappaSD.fit
## 1     0.004809558
## [1] "fitting model: All.X.lser.no.cp.4015.rpart"
## [1] "    indep_vars: age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008"
## + Fold1: cp=5e-05 
## - Fold1: cp=5e-05 
## + Fold2: cp=5e-05 
## - Fold2: cp=5e-05 
## + Fold3: cp=5e-05 
## - Fold3: cp=5e-05 
## + Fold4: cp=5e-05 
## - Fold4: cp=5e-05 
## + Fold5: cp=5e-05 
## - Fold5: cp=5e-05 
## Aggregating results
## Fitting final model on full training set
## Warning: labs do not fit even at cex 0.15, there may be some overplotting
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7, 
##     cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, 
##     surrogatestyle = 0, maxdepth = 30, xval = 0))
##   n= 274803 
## 
##              CP nsplit rel error
## 1  4.908841e-02      0 1.0000000
## 2  1.395884e-02      2 0.9018232
## 3  4.350377e-03      3 0.8878643
## 4  3.597640e-03      4 0.8835140
## 5  9.879673e-04      5 0.8799163
## 6  7.859460e-04     10 0.8748685
## 7  6.789392e-04     11 0.8740826
## 8  4.649258e-04     14 0.8720458
## 9  4.372516e-04     15 0.8715809
## 10 2.988809e-04     19 0.8698319
## 11 2.767415e-04     20 0.8695330
## 12 2.435326e-04     21 0.8692562
## 13 2.036818e-04     22 0.8690127
## 14 1.881842e-04     28 0.8677729
## 15 1.826494e-04     29 0.8675847
## 16 1.605101e-04     31 0.8672194
## 17 1.439056e-04     33 0.8668984
## 18 1.411382e-04     37 0.8663228
## 19 1.217663e-04     42 0.8655922
## 20 1.162314e-04     45 0.8652269
## 21 1.129105e-04     47 0.8649944
## 22 1.051618e-04     52 0.8644299
## 23 9.962695e-05     57 0.8638764
## 24 9.409212e-05     68 0.8627473
## 25 8.855729e-05     74 0.8621827
## 26 8.302246e-05     83 0.8613746
## 27 7.748763e-05     85 0.8612086
## 28 7.379774e-05     97 0.8602455
## 29 7.195280e-05    101 0.8599134
## 30 6.918538e-05    114 0.8588729
## 31 6.641797e-05    122 0.8583194
## 32 6.272808e-05    154 0.8560612
## 33 6.167383e-05    158 0.8557955
## 34 6.088314e-05    166 0.8552752
## 35 5.811572e-05    179 0.8544340
## 36 5.534831e-05    183 0.8542015
## 37 5.313437e-05    228 0.8516001
## 38 5.258089e-05    233 0.8513344
## 39 5.165842e-05    237 0.8511241
## 40 5.000000e-05    254 0.8501832
## 
## Variable importance
## reimbursement2008        bucket2008          diabetes               ihd 
##                31                20                13                13 
##     heart.failure            kidney         arthritis 
##                11                 9                 1 
## 
## Node number 1: 274803 observations,    complexity param=0.04908841
##   predicted class=B1  expected loss=0.3287337  P(node) =1
##     class counts: 184466 52259 24586 11906  1586
##    probabilities: 0.671 0.190 0.089 0.043 0.006 
##   left son=2 (165987 obs) right son=3 (108816 obs)
##   Primary splits:
##       reimbursement2008 < 1565   to the left,  improve=24395.14, (0 missing)
##       bucket2008        < 1.5    to the left,  improve=20624.70, (0 missing)
##       ihd               < 0.5    to the left,  improve=16291.74, (0 missing)
##       diabetes          < 0.5    to the left,  improve=16041.26, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=12498.16, (0 missing)
##   Surrogate splits:
##       bucket2008    < 1.5    to the left,  agree=0.861, adj=0.650, (0 split)
##       ihd           < 0.5    to the left,  agree=0.792, adj=0.474, (0 split)
##       diabetes      < 0.5    to the left,  agree=0.785, adj=0.456, (0 split)
##       heart.failure < 0.5    to the left,  agree=0.762, adj=0.399, (0 split)
##       kidney        < 0.5    to the left,  agree=0.731, adj=0.321, (0 split)
## 
## Node number 2: 165987 observations
##   predicted class=B1  expected loss=0.1261424  P(node) =0.6040218
##     class counts: 145049 12284  6102  2315   237
##    probabilities: 0.874 0.074 0.037 0.014 0.001 
## 
## Node number 3: 108816 observations,    complexity param=0.04908841
##   predicted class=B2  expected loss=0.6326367  P(node) =0.3959782
##     class counts: 39417 39975 18484  9591  1349
##    probabilities: 0.362 0.367 0.170 0.088 0.012 
##   left son=6 (39298 obs) right son=7 (69518 obs)
##   Primary splits:
##       reimbursement2008 < 3065   to the left,  improve=2010.3080, (0 missing)
##       bucket2008        < 1.5    to the left,  improve=1980.9770, (0 missing)
##       kidney            < 0.5    to the left,  improve=1416.9220, (0 missing)
##       diabetes          < 0.5    to the left,  improve=1236.1460, (0 missing)
##       heart.failure     < 0.5    to the left,  improve= 976.9427, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the left,  agree=0.989, adj=0.969, (0 split)
##       ihd        < 0.5    to the left,  agree=0.659, adj=0.056, (0 split)
##       diabetes   < 0.5    to the left,  agree=0.641, adj=0.006, (0 split)
## 
## Node number 6: 39298 observations,    complexity param=0.0006789392
##   predicted class=B1  expected loss=0.4797445  P(node) =0.1430043
##     class counts: 20445 12134  4756  1782   181
##    probabilities: 0.520 0.309 0.121 0.045 0.005 
##   left son=12 (20077 obs) right son=13 (19221 obs)
##   Primary splits:
##       reimbursement2008 < 2175   to the left,  improve=192.7592, (0 missing)
##       diabetes          < 0.5    to the left,  improve=155.3521, (0 missing)
##       ihd               < 0.5    to the left,  improve=114.8541, (0 missing)
##       arthritis         < 0.5    to the left,  improve=114.6837, (0 missing)
##       kidney            < 0.5    to the left,  improve=108.9096, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the left,  agree=0.542, adj=0.063, (0 split)
##       arthritis  < 0.5    to the left,  agree=0.539, adj=0.058, (0 split)
##       ihd        < 0.5    to the left,  agree=0.534, adj=0.048, (0 split)
##       kidney     < 0.5    to the left,  agree=0.532, adj=0.044, (0 split)
##       diabetes   < 0.5    to the left,  agree=0.532, adj=0.043, (0 split)
## 
## Node number 7: 69518 observations,    complexity param=0.01395884
##   predicted class=B2  expected loss=0.5995138  P(node) =0.2529739
##     class counts: 18972 27841 13728  7809  1168
##    probabilities: 0.273 0.400 0.197 0.112 0.017 
##   left son=14 (15717 obs) right son=15 (53801 obs)
##   Primary splits:
##       diabetes      < 0.5    to the left,  improve=646.4740, (0 missing)
##       kidney        < 0.5    to the left,  improve=604.0313, (0 missing)
##       arthritis     < 0.5    to the left,  improve=501.1263, (0 missing)
##       ihd           < 0.5    to the left,  improve=427.9009, (0 missing)
##       heart.failure < 0.5    to the left,  improve=380.0080, (0 missing)
## 
## Node number 12: 20077 observations,    complexity param=9.409212e-05
##   predicted class=B1  expected loss=0.4247148  P(node) =0.07305961
##     class counts: 11550  5416  2200   834    77
##    probabilities: 0.575 0.270 0.110 0.042 0.004 
##   left son=24 (8826 obs) right son=25 (11251 obs)
##   Primary splits:
##       diabetes      < 0.5    to the left,  improve=62.34344, (0 missing)
##       kidney        < 0.5    to the left,  improve=42.15624, (0 missing)
##       ihd           < 0.5    to the left,  improve=40.01287, (0 missing)
##       heart.failure < 0.5    to the left,  improve=36.00697, (0 missing)
##       arthritis     < 0.5    to the left,  improve=33.77686, (0 missing)
##   Surrogate splits:
##       ihd < 0.5    to the left,  agree=0.588, adj=0.062, (0 split)
## 
## Node number 13: 19221 observations,    complexity param=0.0006789392
##   predicted class=B1  expected loss=0.5372249  P(node) =0.06994465
##     class counts:  8895  6718  2556   948   104
##    probabilities: 0.463 0.350 0.133 0.049 0.005 
##   left son=26 (7137 obs) right son=27 (12084 obs)
##   Primary splits:
##       diabetes      < 0.5    to the left,  improve=71.31724, (0 missing)
##       arthritis     < 0.5    to the left,  improve=61.00585, (0 missing)
##       ihd           < 0.5    to the left,  improve=55.20411, (0 missing)
##       heart.failure < 0.5    to the left,  improve=52.20163, (0 missing)
##       kidney        < 0.5    to the left,  improve=49.73230, (0 missing)
## 
## Node number 14: 15717 observations,    complexity param=0.004350377
##   predicted class=B1  expected loss=0.5704651  P(node) =0.0571937
##     class counts:  6751  5490  2365   999   112
##    probabilities: 0.430 0.349 0.150 0.064 0.007 
##   left son=28 (13123 obs) right son=29 (2594 obs)
##   Primary splits:
##       cancer       < 0.5    to the left,  improve=130.12270, (0 missing)
##       arthritis    < 0.5    to the left,  improve=125.41530, (0 missing)
##       ihd          < 0.5    to the left,  improve= 80.76118, (0 missing)
##       depression   < 0.5    to the left,  improve= 61.32779, (0 missing)
##       osteoporosis < 0.5    to the left,  improve= 44.50253, (0 missing)
## 
## Node number 15: 53801 observations,    complexity param=0.0009879673
##   predicted class=B2  expected loss=0.5845616  P(node) =0.1957802
##     class counts: 12221 22351 11363  6810  1056
##    probabilities: 0.227 0.415 0.211 0.127 0.020 
##   left son=30 (25067 obs) right son=31 (28734 obs)
##   Primary splits:
##       kidney            < 0.5    to the left,  improve=408.9756, (0 missing)
##       reimbursement2008 < 15395  to the left,  improve=327.1281, (0 missing)
##       bucket2008        < 3.5    to the left,  improve=313.8191, (0 missing)
##       arthritis         < 0.5    to the left,  improve=266.5595, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=209.4718, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 8365   to the left,  agree=0.666, adj=0.282, (0 split)
##       bucket2008        < 2.5    to the left,  agree=0.664, adj=0.279, (0 split)
##       heart.failure     < 0.5    to the left,  agree=0.628, adj=0.201, (0 split)
##       copd              < 0.5    to the left,  agree=0.595, adj=0.132, (0 split)
##       ihd               < 0.5    to the left,  agree=0.575, adj=0.089, (0 split)
## 
## Node number 24: 8826 observations
##   predicted class=B1  expected loss=0.3716293  P(node) =0.03211755
##     class counts:  5546  2137   805   312    26
##    probabilities: 0.628 0.242 0.091 0.035 0.003 
## 
## Node number 25: 11251 observations,    complexity param=9.409212e-05
##   predicted class=B1  expected loss=0.4663585  P(node) =0.04094206
##     class counts:  6004  3279  1395   522    51
##    probabilities: 0.534 0.291 0.124 0.046 0.005 
##   left son=50 (9007 obs) right son=51 (2244 obs)
##   Primary splits:
##       kidney            < 0.5    to the left,  improve=18.86048, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=17.29926, (0 missing)
##       arthritis         < 0.5    to the left,  improve=16.91283, (0 missing)
##       reimbursement2008 < 1875   to the left,  improve=16.48954, (0 missing)
##       cancer            < 0.5    to the left,  improve=14.98495, (0 missing)
## 
## Node number 26: 7137 observations,    complexity param=0.0001051618
##   predicted class=B1  expected loss=0.470786  P(node) =0.02597133
##     class counts:  3777  2233   794   300    33
##    probabilities: 0.529 0.313 0.111 0.042 0.005 
##   left son=52 (5554 obs) right son=53 (1583 obs)
##   Primary splits:
##       arthritis  < 0.5    to the left,  improve=24.840370, (0 missing)
##       depression < 0.5    to the left,  improve=16.217060, (0 missing)
##       ihd        < 0.5    to the left,  improve=13.895180, (0 missing)
##       copd       < 0.5    to the left,  improve=12.688930, (0 missing)
##       kidney     < 0.5    to the left,  improve= 9.728645, (0 missing)
## 
## Node number 27: 12084 observations,    complexity param=0.0006789392
##   predicted class=B1  expected loss=0.5764647  P(node) =0.04397332
##     class counts:  5118  4485  1762   648    71
##    probabilities: 0.424 0.371 0.146 0.054 0.006 
##   left son=54 (8413 obs) right son=55 (3671 obs)
##   Primary splits:
##       arthritis         < 0.5    to the left,  improve=27.83165, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=26.70933, (0 missing)
##       ihd               < 0.5    to the left,  improve=24.37311, (0 missing)
##       kidney            < 0.5    to the left,  improve=22.60183, (0 missing)
##       reimbursement2008 < 2655   to the left,  improve=21.75660, (0 missing)
## 
## Node number 28: 13123 observations,    complexity param=0.00359764
##   predicted class=B1  expected loss=0.5360055  P(node) =0.04775421
##     class counts:  6089  4435  1751   763    85
##    probabilities: 0.464 0.338 0.133 0.058 0.006 
##   left son=56 (9625 obs) right son=57 (3498 obs)
##   Primary splits:
##       arthritis     < 0.5    to the left,  improve=126.28480, (0 missing)
##       ihd           < 0.5    to the left,  improve= 70.76778, (0 missing)
##       depression    < 0.5    to the left,  improve= 68.94332, (0 missing)
##       osteoporosis  < 0.5    to the left,  improve= 46.31934, (0 missing)
##       heart.failure < 0.5    to the left,  improve= 30.26771, (0 missing)
## 
## Node number 29: 2594 observations,    complexity param=6.167383e-05
##   predicted class=B2  expected loss=0.5932922  P(node) =0.009439489
##     class counts:   662  1055   614   236    27
##    probabilities: 0.255 0.407 0.237 0.091 0.010 
##   left son=58 (1000 obs) right son=59 (1594 obs)
##   Primary splits:
##       reimbursement2008 < 5770   to the left,  improve=8.464458, (0 missing)
##       arthritis         < 0.5    to the left,  improve=7.371565, (0 missing)
##       ihd               < 0.5    to the left,  improve=5.410820, (0 missing)
##       copd              < 0.5    to the left,  improve=5.301788, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.070575, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.823, adj=0.542, (0 split)
## 
## Node number 30: 25067 observations,    complexity param=0.0009879673
##   predicted class=B2  expected loss=0.57091  P(node) =0.09121807
##     class counts:  7517 10756  4691  1917   186
##    probabilities: 0.300 0.429 0.187 0.076 0.007 
##   left son=60 (15178 obs) right son=61 (9889 obs)
##   Primary splits:
##       arthritis     < 0.5    to the left,  improve=169.25970, (0 missing)
##       cancer        < 0.5    to the left,  improve= 99.57556, (0 missing)
##       ihd           < 0.5    to the left,  improve= 68.28883, (0 missing)
##       depression    < 0.5    to the left,  improve= 61.94482, (0 missing)
##       heart.failure < 0.5    to the left,  improve= 42.19646, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 66495  to the left,  agree=0.606, adj=0.001, (0 split)
## 
## Node number 31: 28734 observations,    complexity param=0.0002036818
##   predicted class=B2  expected loss=0.5964711  P(node) =0.1045622
##     class counts:  4704 11595  6672  4893   870
##    probabilities: 0.164 0.404 0.232 0.170 0.030 
##   left son=62 (16249 obs) right son=63 (12485 obs)
##   Primary splits:
##       reimbursement2008 < 15395  to the left,  improve=177.49270, (0 missing)
##       bucket2008        < 3.5    to the left,  improve=170.28940, (0 missing)
##       arthritis         < 0.5    to the left,  improve=101.31920, (0 missing)
##       heart.failure     < 0.5    to the left,  improve= 62.82321, (0 missing)
##       ihd               < 0.5    to the left,  improve= 55.35075, (0 missing)
##   Surrogate splits:
##       bucket2008 < 3.5    to the left,  agree=0.924, adj=0.826, (0 split)
##       copd       < 0.5    to the left,  agree=0.609, adj=0.101, (0 split)
##       stroke     < 0.5    to the left,  agree=0.605, adj=0.091, (0 split)
##       cancer     < 0.5    to the left,  agree=0.580, adj=0.033, (0 split)
##       alzheimers < 0.5    to the left,  agree=0.569, adj=0.008, (0 split)
## 
## Node number 50: 9007 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.4490951  P(node) =0.03277621
##     class counts:  4962  2540  1087   378    40
##    probabilities: 0.551 0.282 0.121 0.042 0.004 
##   left son=100 (4935 obs) right son=101 (4072 obs)
##   Primary splits:
##       reimbursement2008 < 1875   to the left,  improve=14.670650, (0 missing)
##       cancer            < 0.5    to the left,  improve=12.077140, (0 missing)
##       arthritis         < 0.5    to the left,  improve= 9.470091, (0 missing)
##       heart.failure     < 0.5    to the left,  improve= 7.308909, (0 missing)
##       depression        < 0.5    to the left,  improve= 6.801973, (0 missing)
##   Surrogate splits:
##       stroke < 0.5    to the left,  agree=0.549, adj=0.003, (0 split)
##       age    < 29.5   to the right, agree=0.548, adj=0.001, (0 split)
## 
## Node number 51: 2244 observations,    complexity param=9.409212e-05
##   predicted class=B1  expected loss=0.5356506  P(node) =0.00816585
##     class counts:  1042   739   308   144    11
##    probabilities: 0.464 0.329 0.137 0.064 0.005 
##   left son=102 (992 obs) right son=103 (1252 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the left,  improve=7.795458, (0 missing)
##       arthritis         < 0.5    to the left,  improve=7.027320, (0 missing)
##       ihd               < 0.5    to the left,  improve=4.964222, (0 missing)
##       reimbursement2008 < 1735   to the left,  improve=4.132280, (0 missing)
##       cancer            < 0.5    to the left,  improve=3.835396, (0 missing)
##   Surrogate splits:
##       ihd < 0.5    to the left,  agree=0.565, adj=0.016, (0 split)
##       age < 33.5   to the left,  agree=0.559, adj=0.002, (0 split)
## 
## Node number 52: 5554 observations,    complexity param=5.165842e-05
##   predicted class=B1  expected loss=0.4449046  P(node) =0.02021084
##     class counts:  3083  1647   580   217    27
##    probabilities: 0.555 0.297 0.104 0.039 0.005 
##   left son=104 (2348 obs) right son=105 (3206 obs)
##   Primary splits:
##       ihd           < 0.5    to the left,  improve=13.118310, (0 missing)
##       depression    < 0.5    to the left,  improve=12.689550, (0 missing)
##       kidney        < 0.5    to the left,  improve= 9.684755, (0 missing)
##       copd          < 0.5    to the left,  improve= 9.145592, (0 missing)
##       heart.failure < 0.5    to the left,  improve= 8.228139, (0 missing)
##   Surrogate splits:
##       age               < 28.5   to the left,  agree=0.579, adj=0.004, (0 split)
##       reimbursement2008 < 2185   to the left,  agree=0.578, adj=0.001, (0 split)
## 
## Node number 53: 1583 observations,    complexity param=0.0001051618
##   predicted class=B1  expected loss=0.5615919  P(node) =0.00576049
##     class counts:   694   586   214    83     6
##    probabilities: 0.438 0.370 0.135 0.052 0.004 
##   left son=106 (1525 obs) right son=107 (58 obs)
##   Primary splits:
##       stroke            < 0.5    to the left,  improve=5.133391, (0 missing)
##       reimbursement2008 < 2725   to the left,  improve=3.164238, (0 missing)
##       cancer            < 0.5    to the left,  improve=2.451745, (0 missing)
##       copd              < 0.5    to the left,  improve=2.436381, (0 missing)
##       depression        < 0.5    to the left,  improve=1.979459, (0 missing)
## 
## Node number 54: 8413 observations,    complexity param=0.0004372516
##   predicted class=B1  expected loss=0.5530726  P(node) =0.03061466
##     class counts:  3760  2943  1225   438    47
##    probabilities: 0.447 0.350 0.146 0.052 0.006 
##   left son=108 (4375 obs) right son=109 (4038 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the left,  improve=25.12070, (0 missing)
##       ihd               < 0.5    to the left,  improve=19.50225, (0 missing)
##       kidney            < 0.5    to the left,  improve=18.23799, (0 missing)
##       depression        < 0.5    to the left,  improve=14.07225, (0 missing)
##       reimbursement2008 < 2615   to the left,  improve=12.21338, (0 missing)
##   Surrogate splits:
##       kidney     < 0.5    to the left,  agree=0.569, adj=0.103, (0 split)
##       copd       < 0.5    to the left,  agree=0.568, adj=0.100, (0 split)
##       alzheimers < 0.5    to the left,  agree=0.546, adj=0.054, (0 split)
##       ihd        < 0.5    to the left,  agree=0.544, adj=0.050, (0 split)
##       stroke     < 0.5    to the left,  agree=0.536, adj=0.034, (0 split)
## 
## Node number 55: 3671 observations,    complexity param=0.0002988809
##   predicted class=B2  expected loss=0.579951  P(node) =0.01335866
##     class counts:  1358  1542   537   210    24
##    probabilities: 0.370 0.420 0.146 0.057 0.007 
##   left son=110 (2068 obs) right son=111 (1603 obs)
##   Primary splits:
##       reimbursement2008 < 2665   to the left,  improve=10.442080, (0 missing)
##       cancer            < 0.5    to the left,  improve= 4.234333, (0 missing)
##       ihd               < 0.5    to the left,  improve= 4.129116, (0 missing)
##       kidney            < 0.5    to the left,  improve= 3.679214, (0 missing)
##       copd              < 0.5    to the left,  improve= 3.281268, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the left,  agree=0.638, adj=0.170, (0 split)
##       cancer     < 0.5    to the left,  agree=0.566, adj=0.006, (0 split)
##       age        < 26.5   to the right, agree=0.564, adj=0.001, (0 split)
##       stroke     < 0.5    to the left,  agree=0.564, adj=0.001, (0 split)
## 
## Node number 56: 9625 observations,    complexity param=0.0001217663
##   predicted class=B1  expected loss=0.4874805  P(node) =0.03502509
##     class counts:  4933  2954  1162   520    56
##    probabilities: 0.513 0.307 0.121 0.054 0.006 
##   left son=112 (3135 obs) right son=113 (6490 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=51.18602, (0 missing)
##       depression        < 0.5    to the left,  improve=46.82343, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=27.25528, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=25.54800, (0 missing)
##       reimbursement2008 < 6615   to the left,  improve=12.84564, (0 missing)
## 
## Node number 57: 3498 observations,    complexity param=0.0004372516
##   predicted class=B2  expected loss=0.5766152  P(node) =0.01272912
##     class counts:  1156  1481   589   243    29
##    probabilities: 0.330 0.423 0.168 0.069 0.008 
##   left son=114 (2340 obs) right son=115 (1158 obs)
##   Primary splits:
##       reimbursement2008 < 8525   to the left,  improve=12.263650, (0 missing)
##       depression        < 0.5    to the left,  improve=10.454350, (0 missing)
##       bucket2008        < 2.5    to the left,  improve= 9.052395, (0 missing)
##       copd              < 0.5    to the left,  improve= 8.848663, (0 missing)
##       ihd               < 0.5    to the left,  improve= 8.087092, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.979, adj=0.935, (0 split)
##       kidney     < 0.5    to the left,  agree=0.692, adj=0.069, (0 split)
##       stroke     < 0.5    to the left,  agree=0.680, adj=0.033, (0 split)
## 
## Node number 58: 1000 observations
##   predicted class=B2  expected loss=0.562  P(node) =0.00363897
##     class counts:   296   438   191    70     5
##    probabilities: 0.296 0.438 0.191 0.070 0.005 
## 
## Node number 59: 1594 observations,    complexity param=6.167383e-05
##   predicted class=B2  expected loss=0.6129235  P(node) =0.005800519
##     class counts:   366   617   423   166    22
##    probabilities: 0.230 0.387 0.265 0.104 0.014 
##   left son=118 (1054 obs) right son=119 (540 obs)
##   Primary splits:
##       reimbursement2008 < 8645   to the right, improve=7.014383, (0 missing)
##       arthritis         < 0.5    to the left,  improve=5.636989, (0 missing)
##       bucket2008        < 2.5    to the right, improve=4.256675, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=3.245615, (0 missing)
##       ihd               < 0.5    to the left,  improve=2.672736, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.949, adj=0.848, (0 split)
##       age        < 27.5   to the right, agree=0.662, adj=0.002, (0 split)
## 
## Node number 60: 15178 observations,    complexity param=0.0009879673
##   predicted class=B2  expected loss=0.6047569  P(node) =0.05523229
##     class counts:  5388  5999  2649  1047    95
##    probabilities: 0.355 0.395 0.175 0.069 0.006 
##   left son=120 (12572 obs) right son=121 (2606 obs)
##   Primary splits:
##       cancer        < 0.5    to the left,  improve=92.65854, (0 missing)
##       ihd           < 0.5    to the left,  improve=43.72992, (0 missing)
##       depression    < 0.5    to the left,  improve=36.05906, (0 missing)
##       heart.failure < 0.5    to the left,  improve=30.26654, (0 missing)
##       copd          < 0.5    to the left,  improve=25.73984, (0 missing)
## 
## Node number 61: 9889 observations,    complexity param=6.918538e-05
##   predicted class=B2  expected loss=0.5189605  P(node) =0.03598578
##     class counts:  2129  4757  2042   870    91
##    probabilities: 0.215 0.481 0.206 0.088 0.009 
##   left son=122 (5134 obs) right son=123 (4755 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=18.84327, (0 missing)
##       cancer            < 0.5    to the left,  improve=17.45891, (0 missing)
##       ihd               < 0.5    to the left,  improve=13.35120, (0 missing)
##       reimbursement2008 < 9795   to the left,  improve=12.33086, (0 missing)
##       copd              < 0.5    to the left,  improve=12.26415, (0 missing)
##   Surrogate splits:
##       alzheimers        < 0.5    to the left,  agree=0.564, adj=0.093, (0 split)
##       copd              < 0.5    to the left,  agree=0.546, adj=0.056, (0 split)
##       reimbursement2008 < 5815   to the left,  agree=0.542, adj=0.048, (0 split)
##       age               < 64.5   to the right, agree=0.537, adj=0.037, (0 split)
##       bucket2008        < 2.5    to the left,  agree=0.536, adj=0.036, (0 split)
## 
## Node number 62: 16249 observations,    complexity param=0.0001411382
##   predicted class=B2  expected loss=0.5619423  P(node) =0.05912963
##     class counts:  3113  7118  3819  1946   253
##    probabilities: 0.192 0.438 0.235 0.120 0.016 
##   left son=124 (9424 obs) right son=125 (6825 obs)
##   Primary splits:
##       arthritis         < 0.5    to the left,  improve=70.60653, (0 missing)
##       cancer            < 0.5    to the left,  improve=30.24922, (0 missing)
##       ihd               < 0.5    to the left,  improve=29.86941, (0 missing)
##       reimbursement2008 < 5665   to the left,  improve=23.89268, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=21.55872, (0 missing)
## 
## Node number 63: 12485 observations,    complexity param=0.0002036818
##   predicted class=B2  expected loss=0.6414097  P(node) =0.04543255
##     class counts:  1591  4477  2853  2947   617
##    probabilities: 0.127 0.359 0.229 0.236 0.049 
##   left son=126 (5402 obs) right son=127 (7083 obs)
##   Primary splits:
##       arthritis         < 0.5    to the right, improve=35.40534, (0 missing)
##       cancer            < 0.5    to the left,  improve=26.78171, (0 missing)
##       reimbursement2008 < 26625  to the left,  improve=24.60405, (0 missing)
##       depression        < 0.5    to the left,  improve=23.29796, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=17.01274, (0 missing)
##   Surrogate splits:
##       age               < 28.5   to the left,  agree=0.568, adj=0.002, (0 split)
##       reimbursement2008 < 15435  to the left,  agree=0.568, adj=0.001, (0 split)
## 
## Node number 100: 4935 observations
##   predicted class=B1  expected loss=0.4196555  P(node) =0.01795832
##     class counts:  2864  1294   550   205    22
##    probabilities: 0.580 0.262 0.111 0.042 0.004 
## 
## Node number 101: 4072 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.4847741  P(node) =0.01481789
##     class counts:  2098  1246   537   173    18
##    probabilities: 0.515 0.306 0.132 0.042 0.004 
##   left son=202 (3786 obs) right son=203 (286 obs)
##   Primary splits:
##       cancer        < 0.5    to the left,  improve=5.937439, (0 missing)
##       arthritis     < 0.5    to the left,  improve=5.625805, (0 missing)
##       copd          < 0.5    to the left,  improve=3.348444, (0 missing)
##       ihd           < 0.5    to the left,  improve=3.030239, (0 missing)
##       heart.failure < 0.5    to the left,  improve=2.851779, (0 missing)
## 
## Node number 102: 992 observations
##   predicted class=B1  expected loss=0.4808468  P(node) =0.003609859
##     class counts:   515   292   126    57     2
##    probabilities: 0.519 0.294 0.127 0.057 0.002 
## 
## Node number 103: 1252 observations,    complexity param=9.409212e-05
##   predicted class=B1  expected loss=0.5790735  P(node) =0.004555991
##     class counts:   527   447   182    87     9
##    probabilities: 0.421 0.357 0.145 0.069 0.007 
##   left son=206 (904 obs) right son=207 (348 obs)
##   Primary splits:
##       arthritis         < 0.5    to the left,  improve=7.739842, (0 missing)
##       age               < 93.5   to the left,  improve=3.754099, (0 missing)
##       cancer            < 0.5    to the left,  improve=3.514161, (0 missing)
##       reimbursement2008 < 1955   to the left,  improve=3.377454, (0 missing)
##       ihd               < 0.5    to the left,  improve=1.751139, (0 missing)
##   Surrogate splits:
##       age < 30.5   to the right, agree=0.724, adj=0.006, (0 split)
## 
## Node number 104: 2348 observations
##   predicted class=B1  expected loss=0.3973595  P(node) =0.008544303
##     class counts:  1415   632   217    72    12
##    probabilities: 0.603 0.269 0.092 0.031 0.005 
## 
## Node number 105: 3206 observations,    complexity param=5.165842e-05
##   predicted class=B1  expected loss=0.4797255  P(node) =0.01166654
##     class counts:  1668  1015   363   145    15
##    probabilities: 0.520 0.317 0.113 0.045 0.005 
##   left son=210 (2325 obs) right son=211 (881 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=8.135493, (0 missing)
##       kidney            < 0.5    to the left,  improve=5.219511, (0 missing)
##       reimbursement2008 < 2785   to the left,  improve=4.205524, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.201394, (0 missing)
##       copd              < 0.5    to the left,  improve=3.002159, (0 missing)
##   Surrogate splits:
##       age < 29.5   to the right, agree=0.726, adj=0.003, (0 split)
## 
## Node number 106: 1525 observations,    complexity param=0.0001051618
##   predicted class=B1  expected loss=0.5534426  P(node) =0.00554943
##     class counts:   681   554   202    82     6
##    probabilities: 0.447 0.363 0.132 0.054 0.004 
##   left son=212 (1438 obs) right son=213 (87 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=2.548424, (0 missing)
##       reimbursement2008 < 2715   to the right, improve=2.513748, (0 missing)
##       copd              < 0.5    to the left,  improve=1.973703, (0 missing)
##       depression        < 0.5    to the left,  improve=1.853940, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.632947, (0 missing)
## 
## Node number 107: 58 observations
##   predicted class=B2  expected loss=0.4482759  P(node) =0.0002110603
##     class counts:    13    32    12     1     0
##    probabilities: 0.224 0.552 0.207 0.017 0.000 
## 
## Node number 108: 4375 observations,    complexity param=0.0002435326
##   predicted class=B1  expected loss=0.5074286  P(node) =0.0159205
##     class counts:  2155  1478   555   170    17
##    probabilities: 0.493 0.338 0.127 0.039 0.004 
##   left son=216 (3992 obs) right son=217 (383 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=10.015540, (0 missing)
##       ihd               < 0.5    to the left,  improve= 9.488719, (0 missing)
##       depression        < 0.5    to the left,  improve= 7.316301, (0 missing)
##       reimbursement2008 < 2615   to the left,  improve= 5.949976, (0 missing)
##       copd              < 0.5    to the left,  improve= 5.117423, (0 missing)
## 
## Node number 109: 4038 observations,    complexity param=0.0004372516
##   predicted class=B1  expected loss=0.602526  P(node) =0.01469416
##     class counts:  1605  1465   670   268    30
##    probabilities: 0.397 0.363 0.166 0.066 0.007 
##   left son=218 (2819 obs) right son=219 (1219 obs)
##   Primary splits:
##       kidney            < 0.5    to the left,  improve=10.392200, (0 missing)
##       reimbursement2008 < 2455   to the left,  improve= 6.028802, (0 missing)
##       ihd               < 0.5    to the left,  improve= 5.795095, (0 missing)
##       depression        < 0.5    to the left,  improve= 5.214940, (0 missing)
##       stroke            < 0.5    to the left,  improve= 3.343262, (0 missing)
## 
## Node number 110: 2068 observations,    complexity param=0.0002767415
##   predicted class=B1  expected loss=0.5918762  P(node) =0.007525391
##     class counts:   844   817   280   117    10
##    probabilities: 0.408 0.395 0.135 0.057 0.005 
##   left son=220 (517 obs) right son=221 (1551 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=3.581883, (0 missing)
##       reimbursement2008 < 2305   to the left,  improve=3.255344, (0 missing)
##       cancer            < 0.5    to the left,  improve=3.097089, (0 missing)
##       age               < 54.5   to the left,  improve=1.964830, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.730688, (0 missing)
## 
## Node number 111: 1603 observations
##   predicted class=B2  expected loss=0.547723  P(node) =0.00583327
##     class counts:   514   725   257    93    14
##    probabilities: 0.321 0.452 0.160 0.058 0.009 
## 
## Node number 112: 3135 observations,    complexity param=7.19528e-05
##   predicted class=B1  expected loss=0.3974482  P(node) =0.01140817
##     class counts:  1889   825   298   113    10
##    probabilities: 0.603 0.263 0.095 0.036 0.003 
##   left son=224 (2292 obs) right son=225 (843 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=19.892930, (0 missing)
##       reimbursement2008 < 9505   to the right, improve=15.211730, (0 missing)
##       bucket2008        < 2.5    to the right, improve=13.054300, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=10.317040, (0 missing)
##       age               < 92.5   to the left,  improve= 3.244996, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 60755  to the left,  agree=0.731, adj=0.001, (0 split)
## 
## Node number 113: 6490 observations,    complexity param=0.0001217663
##   predicted class=B1  expected loss=0.5309707  P(node) =0.02361692
##     class counts:  3044  2129   864   407    46
##    probabilities: 0.469 0.328 0.133 0.063 0.007 
##   left son=226 (4266 obs) right son=227 (2224 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=22.130520, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=12.472230, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=12.135520, (0 missing)
##       reimbursement2008 < 6615   to the left,  improve=10.028930, (0 missing)
##       bucket2008        < 2.5    to the left,  improve= 8.000565, (0 missing)
##   Surrogate splits:
##       age               < 34.5   to the right, agree=0.658, adj=0.003, (0 split)
##       reimbursement2008 < 115145 to the left,  agree=0.658, adj=0.001, (0 split)
## 
## Node number 114: 2340 observations,    complexity param=5.313437e-05
##   predicted class=B2  expected loss=0.542735  P(node) =0.008515191
##     class counts:   720  1070   391   144    15
##    probabilities: 0.308 0.457 0.167 0.062 0.006 
##   left son=228 (1359 obs) right son=229 (981 obs)
##   Primary splits:
##       reimbursement2008 < 4645   to the left,  improve=5.782135, (0 missing)
##       ihd               < 0.5    to the left,  improve=5.431632, (0 missing)
##       depression        < 0.5    to the left,  improve=4.505952, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=3.336155, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=3.247654, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.613, adj=0.076, (0 split)
##       copd       < 0.5    to the left,  agree=0.606, adj=0.059, (0 split)
##       kidney     < 0.5    to the left,  agree=0.586, adj=0.013, (0 split)
##       age        < 91.5   to the left,  agree=0.585, adj=0.011, (0 split)
##       stroke     < 0.5    to the left,  agree=0.585, adj=0.009, (0 split)
## 
## Node number 115: 1158 observations,    complexity param=0.0004372516
##   predicted class=B1  expected loss=0.6234888  P(node) =0.004213928
##     class counts:   436   411   198    99    14
##    probabilities: 0.377 0.355 0.171 0.085 0.012 
##   left son=230 (714 obs) right son=231 (444 obs)
##   Primary splits:
##       copd              < 0.5    to the left,  improve=13.168040, (0 missing)
##       depression        < 0.5    to the left,  improve= 8.948306, (0 missing)
##       kidney            < 0.5    to the left,  improve= 6.276303, (0 missing)
##       ihd               < 0.5    to the left,  improve= 5.293866, (0 missing)
##       reimbursement2008 < 14980  to the left,  improve= 4.056180, (0 missing)
##   Surrogate splits:
##       age               < 94.5   to the left,  agree=0.626, adj=0.025, (0 split)
##       reimbursement2008 < 72745  to the left,  agree=0.620, adj=0.009, (0 split)
## 
## Node number 118: 1054 observations,    complexity param=6.167383e-05
##   predicted class=B2  expected loss=0.6223909  P(node) =0.003835475
##     class counts:   281   398   250   109    16
##    probabilities: 0.267 0.378 0.237 0.103 0.015 
##   left son=236 (745 obs) right son=237 (309 obs)
##   Primary splits:
##       arthritis         < 0.5    to the left,  improve=8.492364, (0 missing)
##       ihd               < 0.5    to the left,  improve=3.739184, (0 missing)
##       depression        < 0.5    to the left,  improve=2.714506, (0 missing)
##       copd              < 0.5    to the left,  improve=2.704564, (0 missing)
##       reimbursement2008 < 67610  to the left,  improve=2.665770, (0 missing)
##   Surrogate splits:
##       age < 29.5   to the right, agree=0.708, adj=0.003, (0 split)
## 
## Node number 119: 540 observations,    complexity param=6.167383e-05
##   predicted class=B2  expected loss=0.5944444  P(node) =0.001965044
##     class counts:    85   219   173    57     6
##    probabilities: 0.157 0.406 0.320 0.106 0.011 
##   left son=238 (243 obs) right son=239 (297 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the right, improve=3.144781, (0 missing)
##       reimbursement2008 < 7455   to the left,  improve=1.665302, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.352183, (0 missing)
##       age               < 86.5   to the right, improve=1.232072, (0 missing)
##       arthritis         < 0.5    to the right, improve=1.028824, (0 missing)
##   Surrogate splits:
##       copd       < 0.5    to the right, agree=0.604, adj=0.119, (0 split)
##       kidney     < 0.5    to the right, agree=0.585, adj=0.078, (0 split)
##       stroke     < 0.5    to the right, agree=0.583, adj=0.074, (0 split)
##       arthritis  < 0.5    to the right, agree=0.576, adj=0.058, (0 split)
##       depression < 0.5    to the right, agree=0.572, adj=0.049, (0 split)
## 
## Node number 120: 12572 observations,    complexity param=0.0009879673
##   predicted class=B2  expected loss=0.613188  P(node) =0.04574914
##     class counts:  4844  4863  2000   791    74
##    probabilities: 0.385 0.387 0.159 0.063 0.006 
##   left son=240 (2617 obs) right son=241 (9955 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=36.80981, (0 missing)
##       depression        < 0.5    to the left,  improve=36.47326, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=27.52215, (0 missing)
##       copd              < 0.5    to the left,  improve=21.85222, (0 missing)
##       reimbursement2008 < 8955   to the left,  improve=19.34797, (0 missing)
## 
## Node number 121: 2606 observations
##   predicted class=B2  expected loss=0.5640829  P(node) =0.009483157
##     class counts:   544  1136   649   256    21
##    probabilities: 0.209 0.436 0.249 0.098 0.008 
## 
## Node number 122: 5134 observations,    complexity param=6.918538e-05
##   predicted class=B2  expected loss=0.5190884  P(node) =0.01868247
##     class counts:  1277  2469   936   412    40
##    probabilities: 0.249 0.481 0.182 0.080 0.008 
##   left son=244 (4305 obs) right son=245 (829 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=12.348810, (0 missing)
##       reimbursement2008 < 9985   to the left,  improve=11.590150, (0 missing)
##       bucket2008        < 2.5    to the left,  improve= 7.979608, (0 missing)
##       ihd               < 0.5    to the left,  improve= 7.512372, (0 missing)
##       copd              < 0.5    to the left,  improve= 7.186891, (0 missing)
## 
## Node number 123: 4755 observations
##   predicted class=B2  expected loss=0.5188223  P(node) =0.0173033
##     class counts:   852  2288  1106   458    51
##    probabilities: 0.179 0.481 0.233 0.096 0.011 
## 
## Node number 124: 9424 observations,    complexity param=0.0001411382
##   predicted class=B2  expected loss=0.5992148  P(node) =0.03429366
##     class counts:  2192  3777  2139  1156   160
##    probabilities: 0.233 0.401 0.227 0.123 0.017 
##   left son=248 (7786 obs) right son=249 (1638 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=31.06099, (0 missing)
##       ihd               < 0.5    to the left,  improve=19.93184, (0 missing)
##       depression        < 0.5    to the left,  improve=16.57581, (0 missing)
##       reimbursement2008 < 6325   to the left,  improve=12.91187, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=10.82187, (0 missing)
## 
## Node number 125: 6825 observations
##   predicted class=B2  expected loss=0.5104762  P(node) =0.02483597
##     class counts:   921  3341  1680   790    93
##    probabilities: 0.135 0.490 0.246 0.116 0.014 
## 
## Node number 126: 5402 observations,    complexity param=7.748763e-05
##   predicted class=B2  expected loss=0.5960755  P(node) =0.01965772
##     class counts:   509  2182  1310  1186   215
##    probabilities: 0.094 0.404 0.243 0.220 0.040 
##   left son=252 (3345 obs) right son=253 (2057 obs)
##   Primary splits:
##       reimbursement2008 < 34925  to the left,  improve=14.212070, (0 missing)
##       copd              < 0.5    to the left,  improve=10.384850, (0 missing)
##       depression        < 0.5    to the left,  improve= 8.104595, (0 missing)
##       cancer            < 0.5    to the right, improve= 6.743072, (0 missing)
##       heart.failure     < 0.5    to the left,  improve= 6.417519, (0 missing)
##   Surrogate splits:
##       bucket2008 < 4.5    to the left,  agree=0.776, adj=0.413, (0 split)
## 
## Node number 127: 7083 observations,    complexity param=0.0002036818
##   predicted class=B2  expected loss=0.6759848  P(node) =0.02577483
##     class counts:  1082  2295  1543  1761   402
##    probabilities: 0.153 0.324 0.218 0.249 0.057 
##   left son=254 (5298 obs) right son=255 (1785 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=21.09129, (0 missing)
##       depression        < 0.5    to the left,  improve=19.29947, (0 missing)
##       reimbursement2008 < 26625  to the left,  improve=15.18952, (0 missing)
##       copd              < 0.5    to the left,  improve=14.68870, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=12.81802, (0 missing)
## 
## Node number 202: 3786 observations
##   predicted class=B1  expected loss=0.4772847  P(node) =0.01377714
##     class counts:  1979  1131   501   157    18
##    probabilities: 0.523 0.299 0.132 0.041 0.005 
## 
## Node number 203: 286 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5839161  P(node) =0.001040746
##     class counts:   119   115    36    16     0
##    probabilities: 0.416 0.402 0.126 0.056 0.000 
##   left son=406 (128 obs) right son=407 (158 obs)
##   Primary splits:
##       age               < 73.5   to the left,  improve=2.9724540, (0 missing)
##       reimbursement2008 < 2005   to the left,  improve=1.9802050, (0 missing)
##       depression        < 0.5    to the left,  improve=0.5460014, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.4144954, (0 missing)
##       ihd               < 0.5    to the left,  improve=0.3767582, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 1945   to the left,  agree=0.580, adj=0.063, (0 split)
##       arthritis         < 0.5    to the right, agree=0.563, adj=0.023, (0 split)
## 
## Node number 206: 904 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5376106  P(node) =0.003289629
##     class counts:   418   304   119    57     6
##    probabilities: 0.462 0.336 0.132 0.063 0.007 
##   left son=412 (270 obs) right son=413 (634 obs)
##   Primary splits:
##       reimbursement2008 < 1735   to the left,  improve=3.8438620, (0 missing)
##       age               < 93.5   to the left,  improve=3.6681650, (0 missing)
##       ihd               < 0.5    to the left,  improve=3.2669730, (0 missing)
##       cancer            < 0.5    to the left,  improve=3.0869480, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.7446912, (0 missing)
##   Surrogate splits:
##       age < 29     to the left,  agree=0.702, adj=0.004, (0 split)
## 
## Node number 207: 348 observations
##   predicted class=B2  expected loss=0.5890805  P(node) =0.001266362
##     class counts:   109   143    63    30     3
##    probabilities: 0.313 0.411 0.181 0.086 0.009 
## 
## Node number 210: 2325 observations
##   predicted class=B1  expected loss=0.4541935  P(node) =0.008460606
##     class counts:  1269   700   245    99    12
##    probabilities: 0.546 0.301 0.105 0.043 0.005 
## 
## Node number 211: 881 observations,    complexity param=5.165842e-05
##   predicted class=B1  expected loss=0.5471056  P(node) =0.003205933
##     class counts:   399   315   118    46     3
##    probabilities: 0.453 0.358 0.134 0.052 0.003 
##   left son=422 (763 obs) right son=423 (118 obs)
##   Primary splits:
##       kidney            < 0.5    to the left,  improve=3.122415, (0 missing)
##       age               < 78.5   to the right, improve=2.656467, (0 missing)
##       reimbursement2008 < 2205   to the right, improve=1.600090, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.074836, (0 missing)
##       bucket2008        < 1.5    to the left,  improve=1.071176, (0 missing)
## 
## Node number 212: 1438 observations,    complexity param=8.855729e-05
##   predicted class=B1  expected loss=0.5452017  P(node) =0.00523284
##     class counts:   654   515   187    76     6
##    probabilities: 0.455 0.358 0.130 0.053 0.004 
##   left son=424 (495 obs) right son=425 (943 obs)
##   Primary splits:
##       reimbursement2008 < 2715   to the right, improve=2.835023, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.879898, (0 missing)
##       copd              < 0.5    to the left,  improve=1.857999, (0 missing)
##       age               < 40.5   to the right, improve=1.802592, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.761837, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the right, agree=0.713, adj=0.166, (0 split)
##       age        < 28.5   to the left,  agree=0.656, adj=0.002, (0 split)
## 
## Node number 213: 87 observations
##   predicted class=B2  expected loss=0.5517241  P(node) =0.0003165904
##     class counts:    27    39    15     6     0
##    probabilities: 0.310 0.448 0.172 0.069 0.000 
## 
## Node number 216: 3992 observations,    complexity param=6.918538e-05
##   predicted class=B1  expected loss=0.495491  P(node) =0.01452677
##     class counts:  2014  1315   497   153    13
##    probabilities: 0.505 0.329 0.124 0.038 0.003 
##   left son=432 (1265 obs) right son=433 (2727 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=7.867939, (0 missing)
##       depression        < 0.5    to the left,  improve=6.016589, (0 missing)
##       copd              < 0.5    to the left,  improve=5.402587, (0 missing)
##       kidney            < 0.5    to the left,  improve=3.916699, (0 missing)
##       reimbursement2008 < 2615   to the left,  improve=3.836002, (0 missing)
## 
## Node number 217: 383 observations,    complexity param=0.0001826494
##   predicted class=B2  expected loss=0.5744125  P(node) =0.001393726
##     class counts:   141   163    58    17     4
##    probabilities: 0.368 0.426 0.151 0.044 0.010 
##   left son=434 (238 obs) right son=435 (145 obs)
##   Primary splits:
##       reimbursement2008 < 2705   to the left,  improve=4.9624930, (0 missing)
##       depression        < 0.5    to the left,  improve=3.2303380, (0 missing)
##       age               < 67.5   to the right, improve=2.3511250, (0 missing)
##       ihd               < 0.5    to the left,  improve=1.5735720, (0 missing)
##       bucket2008        < 1.5    to the left,  improve=0.9813303, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the left,  agree=0.681, adj=0.159, (0 split)
##       age        < 45.5   to the right, agree=0.624, adj=0.007, (0 split)
## 
## Node number 218: 2819 observations,    complexity param=0.0001129105
##   predicted class=B1  expected loss=0.5746719  P(node) =0.01025826
##     class counts:  1199   980   439   183    18
##    probabilities: 0.425 0.348 0.156 0.065 0.006 
##   left son=436 (635 obs) right son=437 (2184 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=6.072389, (0 missing)
##       reimbursement2008 < 2325   to the left,  improve=3.797765, (0 missing)
##       age               < 40.5   to the right, improve=3.110525, (0 missing)
##       depression        < 0.5    to the left,  improve=2.993563, (0 missing)
##       stroke            < 0.5    to the left,  improve=2.412511, (0 missing)
## 
## Node number 219: 1219 observations,    complexity param=8.855729e-05
##   predicted class=B2  expected loss=0.6021329  P(node) =0.004435905
##     class counts:   406   485   231    85    12
##    probabilities: 0.333 0.398 0.189 0.070 0.010 
##   left son=438 (613 obs) right son=439 (606 obs)
##   Primary splits:
##       reimbursement2008 < 2615   to the left,  improve=4.2080810, (0 missing)
##       age               < 98.5   to the right, improve=2.1482090, (0 missing)
##       depression        < 0.5    to the left,  improve=1.6601240, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.8099205, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=0.7434054, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the left,  agree=0.579, adj=0.153, (0 split)
##       depression < 0.5    to the left,  agree=0.523, adj=0.041, (0 split)
##       stroke     < 0.5    to the left,  agree=0.522, adj=0.038, (0 split)
##       age        < 65.5   to the right, agree=0.519, adj=0.033, (0 split)
##       cancer     < 0.5    to the left,  agree=0.514, adj=0.021, (0 split)
## 
## Node number 220: 517 observations,    complexity param=7.19528e-05
##   predicted class=B1  expected loss=0.5299807  P(node) =0.001881348
##     class counts:   243   191    57    25     1
##    probabilities: 0.470 0.369 0.110 0.048 0.002 
##   left son=440 (143 obs) right son=441 (374 obs)
##   Primary splits:
##       reimbursement2008 < 2295   to the left,  improve=6.0966680, (0 missing)
##       cancer            < 0.5    to the left,  improve=2.5628030, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.9493160, (0 missing)
##       age               < 44.5   to the right, improve=1.5968610, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.9005685, (0 missing)
##   Surrogate splits:
##       age < 98.5   to the right, agree=0.729, adj=0.021, (0 split)
## 
## Node number 221: 1551 observations,    complexity param=9.962695e-05
##   predicted class=B2  expected loss=0.5963894  P(node) =0.005644043
##     class counts:   601   626   223    92     9
##    probabilities: 0.387 0.404 0.144 0.059 0.006 
##   left son=442 (18 obs) right son=443 (1533 obs)
##   Primary splits:
##       age    < 35     to the left,  improve=3.0170030, (0 missing)
##       kidney < 0.5    to the left,  improve=2.3281310, (0 missing)
##       cancer < 0.5    to the left,  improve=1.5502140, (0 missing)
##       stroke < 0.5    to the left,  improve=1.1903410, (0 missing)
##       copd   < 0.5    to the left,  improve=0.9727402, (0 missing)
## 
## Node number 224: 2292 observations
##   predicted class=B1  expected loss=0.3582024  P(node) =0.00834052
##     class counts:  1471   549   183    79    10
##    probabilities: 0.642 0.240 0.080 0.034 0.004 
## 
## Node number 225: 843 observations,    complexity param=7.19528e-05
##   predicted class=B1  expected loss=0.5041518  P(node) =0.003067652
##     class counts:   418   276   115    34     0
##    probabilities: 0.496 0.327 0.136 0.040 0.000 
##   left son=450 (810 obs) right son=451 (33 obs)
##   Primary splits:
##       age               < 92.5   to the left,  improve=5.7055350, (0 missing)
##       reimbursement2008 < 11540  to the right, improve=5.6370950, (0 missing)
##       bucket2008        < 2.5    to the right, improve=2.9317810, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.7284423, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.3506867, (0 missing)
## 
## Node number 226: 4266 observations,    complexity param=0.0001051618
##   predicted class=B1  expected loss=0.4946085  P(node) =0.01552385
##     class counts:  2156  1343   503   238    26
##    probabilities: 0.505 0.315 0.118 0.056 0.006 
##   left son=452 (3304 obs) right son=453 (962 obs)
##   Primary splits:
##       osteoporosis      < 0.5    to the left,  improve=10.212680, (0 missing)
##       reimbursement2008 < 5905   to the right, improve= 9.673580, (0 missing)
##       bucket2008        < 2.5    to the right, improve= 7.844764, (0 missing)
##       heart.failure     < 0.5    to the left,  improve= 6.371374, (0 missing)
##       age               < 62.5   to the left,  improve= 3.683231, (0 missing)
## 
## Node number 227: 2224 observations,    complexity param=0.0001217663
##   predicted class=B1  expected loss=0.6007194  P(node) =0.00809307
##     class counts:   888   786   361   169    20
##    probabilities: 0.399 0.353 0.162 0.076 0.009 
##   left son=454 (1518 obs) right son=455 (706 obs)
##   Primary splits:
##       kidney            < 0.5    to the left,  improve=6.746609, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=4.569316, (0 missing)
##       reimbursement2008 < 10710  to the left,  improve=3.711923, (0 missing)
##       age               < 39.5   to the right, improve=3.285727, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=2.661027, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 14380  to the left,  agree=0.714, adj=0.101, (0 split)
##       bucket2008        < 3.5    to the left,  agree=0.708, adj=0.081, (0 split)
##       age               < 98.5   to the left,  agree=0.684, adj=0.004, (0 split)
## 
## Node number 228: 1359 observations,    complexity param=5.313437e-05
##   predicted class=B2  expected loss=0.5548197  P(node) =0.004945361
##     class counts:   467   605   203    76     8
##    probabilities: 0.344 0.445 0.149 0.056 0.006 
##   left son=456 (440 obs) right son=457 (919 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=3.300387, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=2.001264, (0 missing)
##       reimbursement2008 < 3265   to the left,  improve=1.998939, (0 missing)
##       depression        < 0.5    to the left,  improve=1.755319, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.574681, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 3095   to the left,  agree=0.678, adj=0.007, (0 split)
##       age               < 29.5   to the left,  agree=0.678, adj=0.005, (0 split)
## 
## Node number 229: 981 observations
##   predicted class=B2  expected loss=0.5259939  P(node) =0.00356983
##     class counts:   253   465   188    68     7
##    probabilities: 0.258 0.474 0.192 0.069 0.007 
## 
## Node number 230: 714 observations,    complexity param=0.0001881842
##   predicted class=B1  expected loss=0.5546218  P(node) =0.002598225
##     class counts:   318   239    91    61     5
##    probabilities: 0.445 0.335 0.127 0.085 0.007 
##   left son=460 (412 obs) right son=461 (302 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=8.699660, (0 missing)
##       age               < 92.5   to the right, improve=3.253447, (0 missing)
##       reimbursement2008 < 14980  to the left,  improve=2.826720, (0 missing)
##       bucket2008        < 3.5    to the left,  improve=2.191697, (0 missing)
##       kidney            < 0.5    to the left,  improve=2.037790, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 32685  to the left,  agree=0.591, adj=0.033, (0 split)
##       age               < 35.5   to the right, agree=0.583, adj=0.013, (0 split)
## 
## Node number 231: 444 observations,    complexity param=6.088314e-05
##   predicted class=B2  expected loss=0.6126126  P(node) =0.001615703
##     class counts:   118   172   107    38     9
##    probabilities: 0.266 0.387 0.241 0.086 0.020 
##   left son=462 (282 obs) right son=463 (162 obs)
##   Primary splits:
##       osteoporosis      < 0.5    to the left,  improve=3.735228, (0 missing)
##       kidney            < 0.5    to the left,  improve=3.274615, (0 missing)
##       reimbursement2008 < 68975  to the right, improve=3.185223, (0 missing)
##       ihd               < 0.5    to the left,  improve=3.085645, (0 missing)
##       age               < 76.5   to the left,  improve=1.652811, (0 missing)
##   Surrogate splits:
##       age               < 95.5   to the left,  agree=0.644, adj=0.025, (0 split)
##       reimbursement2008 < 8635   to the right, agree=0.637, adj=0.006, (0 split)
## 
## Node number 236: 745 observations,    complexity param=6.167383e-05
##   predicted class=B2  expected loss=0.6228188  P(node) =0.002711033
##     class counts:   232   281   150    72    10
##    probabilities: 0.311 0.377 0.201 0.097 0.013 
##   left son=472 (159 obs) right son=473 (586 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=3.002920, (0 missing)
##       reimbursement2008 < 58135  to the left,  improve=2.259882, (0 missing)
##       depression        < 0.5    to the left,  improve=2.111862, (0 missing)
##       bucket2008        < 4.5    to the left,  improve=1.991400, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.920660, (0 missing)
## 
## Node number 237: 309 observations,    complexity param=6.167383e-05
##   predicted class=B2  expected loss=0.6213592  P(node) =0.001124442
##     class counts:    49   117   100    37     6
##    probabilities: 0.159 0.379 0.324 0.120 0.019 
##   left son=474 (237 obs) right son=475 (72 obs)
##   Primary splits:
##       reimbursement2008 < 10960  to the right, improve=2.966323, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.571780, (0 missing)
##       age               < 90.5   to the left,  improve=1.407411, (0 missing)
##       copd              < 0.5    to the left,  improve=1.306020, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.907593, (0 missing)
## 
## Node number 238: 243 observations
##   predicted class=B2  expected loss=0.526749  P(node) =0.0008842698
##     class counts:    33   115    67    24     4
##    probabilities: 0.136 0.473 0.276 0.099 0.016 
## 
## Node number 239: 297 observations,    complexity param=6.167383e-05
##   predicted class=B3  expected loss=0.6430976  P(node) =0.001080774
##     class counts:    52   104   106    33     2
##    probabilities: 0.175 0.350 0.357 0.111 0.007 
##   left son=478 (226 obs) right son=479 (71 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=1.480103, (0 missing)
##       reimbursement2008 < 6875   to the right, improve=1.383473, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.357727, (0 missing)
##       age               < 54     to the left,  improve=1.263809, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.096200, (0 missing)
## 
## Node number 240: 2617 observations,    complexity param=0.0001439056
##   predicted class=B1  expected loss=0.5257929  P(node) =0.009523186
##     class counts:  1241   884   351   127    14
##    probabilities: 0.474 0.338 0.134 0.049 0.005 
##   left son=480 (403 obs) right son=481 (2214 obs)
##   Primary splits:
##       reimbursement2008 < 9400   to the right, improve=12.428110, (0 missing)
##       bucket2008        < 2.5    to the right, improve= 8.843694, (0 missing)
##       depression        < 0.5    to the left,  improve= 8.588030, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve= 8.405901, (0 missing)
##       alzheimers        < 0.5    to the left,  improve= 4.036896, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.947, adj=0.658, (0 split)
## 
## Node number 241: 9955 observations,    complexity param=0.0009879673
##   predicted class=B2  expected loss=0.6003014  P(node) =0.03622595
##     class counts:  3603  3979  1649   664    60
##    probabilities: 0.362 0.400 0.166 0.067 0.006 
##   left son=482 (5563 obs) right son=483 (4392 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=24.69099, (0 missing)
##       copd              < 0.5    to the left,  improve=17.49244, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=17.05734, (0 missing)
##       reimbursement2008 < 8955   to the left,  improve=14.88623, (0 missing)
##       bucket2008        < 2.5    to the left,  improve= 9.99202, (0 missing)
##   Surrogate splits:
##       alzheimers        < 0.5    to the left,  agree=0.574, adj=0.034, (0 split)
##       age               < 47.5   to the right, agree=0.565, adj=0.013, (0 split)
##       copd              < 0.5    to the left,  agree=0.564, adj=0.013, (0 split)
##       reimbursement2008 < 13565  to the left,  agree=0.561, adj=0.005, (0 split)
##       bucket2008        < 3.5    to the left,  agree=0.559, adj=0.001, (0 split)
## 
## Node number 244: 4305 observations,    complexity param=6.918538e-05
##   predicted class=B2  expected loss=0.524971  P(node) =0.01566577
##     class counts:  1149  2045   746   328    37
##    probabilities: 0.267 0.475 0.173 0.076 0.009 
##   left son=488 (1063 obs) right son=489 (3242 obs)
##   Primary splits:
##       reimbursement2008 < 9880   to the right, improve=11.346300, (0 missing)
##       bucket2008        < 2.5    to the right, improve= 8.562449, (0 missing)
##       ihd               < 0.5    to the left,  improve= 7.353611, (0 missing)
##       copd              < 0.5    to the left,  improve= 6.701463, (0 missing)
##       heart.failure     < 0.5    to the left,  improve= 3.881008, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.941, adj=0.762, (0 split)
## 
## Node number 245: 829 observations
##   predicted class=B2  expected loss=0.4885404  P(node) =0.003016707
##     class counts:   128   424   190    84     3
##    probabilities: 0.154 0.511 0.229 0.101 0.004 
## 
## Node number 248: 7786 observations,    complexity param=0.0001411382
##   predicted class=B2  expected loss=0.6050604  P(node) =0.02833302
##     class counts:  1982  3075  1667   929   133
##    probabilities: 0.255 0.395 0.214 0.119 0.017 
##   left son=496 (964 obs) right son=497 (6822 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=18.914850, (0 missing)
##       depression        < 0.5    to the left,  improve=16.457650, (0 missing)
##       reimbursement2008 < 6325   to the left,  improve=12.927220, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve= 9.344273, (0 missing)
##       bucket2008        < 2.5    to the left,  improve= 9.314433, (0 missing)
## 
## Node number 249: 1638 observations
##   predicted class=B2  expected loss=0.5714286  P(node) =0.005960634
##     class counts:   210   702   472   227    27
##    probabilities: 0.128 0.429 0.288 0.139 0.016 
## 
## Node number 252: 3345 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.580568  P(node) =0.01217236
##     class counts:   372  1403   837   632   101
##    probabilities: 0.111 0.419 0.250 0.189 0.030 
##   left son=504 (1291 obs) right son=505 (2054 obs)
##   Primary splits:
##       depression    < 0.5    to the left,  improve=6.733363, (0 missing)
##       copd          < 0.5    to the left,  improve=6.399894, (0 missing)
##       cancer        < 0.5    to the left,  improve=5.398776, (0 missing)
##       heart.failure < 0.5    to the left,  improve=3.401421, (0 missing)
##       age           < 31.5   to the right, improve=3.041832, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 15665  to the left,  agree=0.614, adj=0.001, (0 split)
## 
## Node number 253: 2057 observations,    complexity param=7.748763e-05
##   predicted class=B2  expected loss=0.6212931  P(node) =0.007485362
##     class counts:   137   779   473   554   114
##    probabilities: 0.067 0.379 0.230 0.269 0.055 
##   left son=506 (520 obs) right son=507 (1537 obs)
##   Primary splits:
##       copd          < 0.5    to the left,  improve=4.741452, (0 missing)
##       age           < 62.5   to the right, improve=3.709690, (0 missing)
##       cancer        < 0.5    to the right, improve=3.631891, (0 missing)
##       ihd           < 0.5    to the left,  improve=3.269099, (0 missing)
##       heart.failure < 0.5    to the left,  improve=3.168350, (0 missing)
##   Surrogate splits:
##       heart.failure < 0.5    to the left,  agree=0.751, adj=0.015, (0 split)
##       age           < 29     to the left,  agree=0.749, adj=0.008, (0 split)
##       ihd           < 0.5    to the left,  agree=0.749, adj=0.008, (0 split)
## 
## Node number 254: 5298 observations,    complexity param=0.0002036818
##   predicted class=B2  expected loss=0.689128  P(node) =0.01927927
##     class counts:   908  1647  1051  1364   328
##    probabilities: 0.171 0.311 0.198 0.257 0.062 
##   left son=508 (2489 obs) right son=509 (2809 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=18.17296, (0 missing)
##       reimbursement2008 < 22335  to the left,  improve=13.06444, (0 missing)
##       copd              < 0.5    to the left,  improve=11.53148, (0 missing)
##       ihd               < 0.5    to the left,  improve= 8.63716, (0 missing)
##       heart.failure     < 0.5    to the left,  improve= 8.42218, (0 missing)
##   Surrogate splits:
##       copd              < 0.5    to the left,  agree=0.579, adj=0.104, (0 split)
##       alzheimers        < 0.5    to the left,  agree=0.573, adj=0.092, (0 split)
##       ihd               < 0.5    to the left,  agree=0.545, adj=0.033, (0 split)
##       heart.failure     < 0.5    to the left,  agree=0.544, adj=0.030, (0 split)
##       reimbursement2008 < 16955  to the left,  agree=0.535, adj=0.010, (0 split)
## 
## Node number 255: 1785 observations
##   predicted class=B2  expected loss=0.6369748  P(node) =0.006495562
##     class counts:   174   648   492   397    74
##    probabilities: 0.097 0.363 0.276 0.222 0.041 
## 
## Node number 406: 128 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5  P(node) =0.0004657882
##     class counts:    64    42    14     8     0
##    probabilities: 0.500 0.328 0.109 0.062 0.000 
##   left son=812 (95 obs) right son=813 (33 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=3.3313600, (0 missing)
##       reimbursement2008 < 2155   to the left,  improve=2.1875000, (0 missing)
##       age               < 70.5   to the right, improve=1.5228130, (0 missing)
##       arthritis         < 0.5    to the left,  improve=1.1806970, (0 missing)
##       copd              < 0.5    to the left,  improve=0.4207762, (0 missing)
## 
## Node number 407: 158 observations
##   predicted class=B2  expected loss=0.5379747  P(node) =0.0005749573
##     class counts:    55    73    22     8     0
##    probabilities: 0.348 0.462 0.139 0.051 0.000 
## 
## Node number 412: 270 observations
##   predicted class=B1  expected loss=0.462963  P(node) =0.000982522
##     class counts:   145    73    36    15     1
##    probabilities: 0.537 0.270 0.133 0.056 0.004 
## 
## Node number 413: 634 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5694006  P(node) =0.002307107
##     class counts:   273   231    83    42     5
##    probabilities: 0.431 0.364 0.131 0.066 0.008 
##   left son=826 (596 obs) right son=827 (38 obs)
##   Primary splits:
##       age               < 91.5   to the left,  improve=3.6059530, (0 missing)
##       ihd               < 0.5    to the left,  improve=2.2411130, (0 missing)
##       reimbursement2008 < 1765   to the right, improve=2.0115470, (0 missing)
##       cancer            < 0.5    to the left,  improve=1.8824720, (0 missing)
##       depression        < 0.5    to the right, improve=0.5863526, (0 missing)
## 
## Node number 422: 763 observations
##   predicted class=B1  expected loss=0.5307995  P(node) =0.002776534
##     class counts:   358   260   102    40     3
##    probabilities: 0.469 0.341 0.134 0.052 0.004 
## 
## Node number 423: 118 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5338983  P(node) =0.0004293985
##     class counts:    41    55    16     6     0
##    probabilities: 0.347 0.466 0.136 0.051 0.000 
##   left son=846 (22 obs) right son=847 (96 obs)
##   Primary splits:
##       reimbursement2008 < 2865   to the right, improve=2.6611130, (0 missing)
##       copd              < 0.5    to the left,  improve=1.5528850, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.3108310, (0 missing)
##       bucket2008        < 1.5    to the left,  improve=1.2553930, (0 missing)
##       age               < 89.5   to the left,  improve=0.9696791, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the right, agree=0.873, adj=0.318, (0 split)
## 
## Node number 424: 495 observations,    complexity param=8.855729e-05
##   predicted class=B1  expected loss=0.5656566  P(node) =0.00180129
##     class counts:   215   202    50    26     2
##    probabilities: 0.434 0.408 0.101 0.053 0.004 
##   left son=848 (385 obs) right son=849 (110 obs)
##   Primary splits:
##       reimbursement2008 < 2795   to the right, improve=2.2427130, (0 missing)
##       age               < 73.5   to the left,  improve=1.5903260, (0 missing)
##       ihd               < 0.5    to the left,  improve=1.1717170, (0 missing)
##       depression        < 0.5    to the left,  improve=0.5724615, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.5572971, (0 missing)
## 
## Node number 425: 943 observations
##   predicted class=B1  expected loss=0.5344645  P(node) =0.003431549
##     class counts:   439   313   137    50     4
##    probabilities: 0.466 0.332 0.145 0.053 0.004 
## 
## Node number 432: 1265 observations
##   predicted class=B1  expected loss=0.4442688  P(node) =0.004603298
##     class counts:   703   367   147    44     4
##    probabilities: 0.556 0.290 0.116 0.035 0.003 
## 
## Node number 433: 2727 observations,    complexity param=6.918538e-05
##   predicted class=B1  expected loss=0.5192519  P(node) =0.009923472
##     class counts:  1311   948   350   109     9
##    probabilities: 0.481 0.348 0.128 0.040 0.003 
##   left son=866 (1499 obs) right son=867 (1228 obs)
##   Primary splits:
##       reimbursement2008 < 2615   to the left,  improve=4.028460, (0 missing)
##       age               < 54.5   to the left,  improve=3.426946, (0 missing)
##       copd              < 0.5    to the left,  improve=2.215284, (0 missing)
##       stroke            < 0.5    to the left,  improve=2.121876, (0 missing)
##       depression        < 0.5    to the left,  improve=1.918448, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the left,  agree=0.612, adj=0.139, (0 split)
##       age        < 97.5   to the left,  agree=0.552, adj=0.005, (0 split)
## 
## Node number 434: 238 observations,    complexity param=0.0001826494
##   predicted class=B1  expected loss=0.5714286  P(node) =0.000866075
##     class counts:   102    86    38     9     3
##    probabilities: 0.429 0.361 0.160 0.038 0.013 
##   left son=868 (167 obs) right son=869 (71 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=4.834875, (0 missing)
##       age               < 59.5   to the right, improve=2.461134, (0 missing)
##       ihd               < 0.5    to the left,  improve=1.909944, (0 missing)
##       reimbursement2008 < 2285   to the left,  improve=1.842456, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.113912, (0 missing)
##   Surrogate splits:
##       age < 97.5   to the left,  agree=0.718, adj=0.056, (0 split)
## 
## Node number 435: 145 observations
##   predicted class=B2  expected loss=0.4689655  P(node) =0.0005276507
##     class counts:    39    77    20     8     1
##    probabilities: 0.269 0.531 0.138 0.055 0.007 
## 
## Node number 436: 635 observations
##   predicted class=B1  expected loss=0.5023622  P(node) =0.002310746
##     class counts:   316   196    93    26     4
##    probabilities: 0.498 0.309 0.146 0.041 0.006 
## 
## Node number 437: 2184 observations,    complexity param=0.0001129105
##   predicted class=B1  expected loss=0.595696  P(node) =0.007947511
##     class counts:   883   784   346   157    14
##    probabilities: 0.404 0.359 0.158 0.072 0.006 
##   left son=874 (393 obs) right son=875 (1791 obs)
##   Primary splits:
##       reimbursement2008 < 2315   to the left,  improve=4.386891, (0 missing)
##       depression        < 0.5    to the left,  improve=4.376862, (0 missing)
##       age               < 39.5   to the right, improve=3.004733, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=2.391734, (0 missing)
##       stroke            < 0.5    to the left,  improve=2.171601, (0 missing)
## 
## Node number 438: 613 observations,    complexity param=8.302246e-05
##   predicted class=B1  expected loss=0.6182708  P(node) =0.002230689
##     class counts:   234   226   111    36     6
##    probabilities: 0.382 0.369 0.181 0.059 0.010 
##   left son=876 (180 obs) right son=877 (433 obs)
##   Primary splits:
##       osteoporosis      < 0.5    to the right, improve=1.4494640, (0 missing)
##       age               < 98.5   to the right, improve=1.3979840, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.9190213, (0 missing)
##       reimbursement2008 < 2275   to the left,  improve=0.8284921, (0 missing)
##       depression        < 0.5    to the left,  improve=0.7804891, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 2605   to the right, agree=0.713, adj=0.022, (0 split)
##       age               < 99.5   to the right, agree=0.711, adj=0.017, (0 split)
## 
## Node number 439: 606 observations
##   predicted class=B2  expected loss=0.5726073  P(node) =0.002205216
##     class counts:   172   259   120    49     6
##    probabilities: 0.284 0.427 0.198 0.081 0.010 
## 
## Node number 440: 143 observations
##   predicted class=B1  expected loss=0.3986014  P(node) =0.0005203728
##     class counts:    86    37    11     9     0
##    probabilities: 0.601 0.259 0.077 0.063 0.000 
## 
## Node number 441: 374 observations,    complexity param=7.19528e-05
##   predicted class=B1  expected loss=0.5802139  P(node) =0.001360975
##     class counts:   157   154    46    16     1
##    probabilities: 0.420 0.412 0.123 0.043 0.003 
##   left son=882 (25 obs) right son=883 (349 obs)
##   Primary splits:
##       reimbursement2008 < 2315   to the left,  improve=4.569334, (0 missing)
##       cancer            < 0.5    to the left,  improve=2.355946, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.361181, (0 missing)
##       age               < 90.5   to the right, improve=1.103565, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.082873, (0 missing)
## 
## Node number 442: 18 observations
##   predicted class=B1  expected loss=0.2777778  P(node) =6.550147e-05
##     class counts:    13     4     0     1     0
##    probabilities: 0.722 0.222 0.000 0.056 0.000 
## 
## Node number 443: 1533 observations,    complexity param=7.748763e-05
##   predicted class=B2  expected loss=0.5942596  P(node) =0.005578542
##     class counts:   588   622   223    91     9
##    probabilities: 0.384 0.406 0.145 0.059 0.006 
##   left son=886 (1101 obs) right son=887 (432 obs)
##   Primary splits:
##       kidney            < 0.5    to the left,  improve=2.2490350, (0 missing)
##       cancer            < 0.5    to the left,  improve=1.4724050, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.3260620, (0 missing)
##       reimbursement2008 < 2435   to the left,  improve=1.1404580, (0 missing)
##       copd              < 0.5    to the left,  improve=0.9660973, (0 missing)
## 
## Node number 450: 810 observations,    complexity param=5.258089e-05
##   predicted class=B1  expected loss=0.491358  P(node) =0.002947566
##     class counts:   412   257   108    33     0
##    probabilities: 0.509 0.317 0.133 0.041 0.000 
##   left son=900 (117 obs) right son=901 (693 obs)
##   Primary splits:
##       reimbursement2008 < 11525  to the right, improve=5.1504220, (0 missing)
##       bucket2008        < 2.5    to the right, improve=2.8801500, (0 missing)
##       age               < 34.5   to the left,  improve=1.2970260, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.8677994, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.5279869, (0 missing)
##   Surrogate splits:
##       bucket2008 < 3.5    to the right, agree=0.928, adj=0.504, (0 split)
## 
## Node number 451: 33 observations
##   predicted class=B2  expected loss=0.4242424  P(node) =0.000120086
##     class counts:     6    19     7     1     0
##    probabilities: 0.182 0.576 0.212 0.030 0.000 
## 
## Node number 452: 3304 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.4757869  P(node) =0.01202316
##     class counts:  1732   979   389   183    21
##    probabilities: 0.524 0.296 0.118 0.055 0.006 
##   left son=904 (1626 obs) right son=905 (1678 obs)
##   Primary splits:
##       reimbursement2008 < 5905   to the right, improve=9.971499, (0 missing)
##       bucket2008        < 2.5    to the right, improve=7.851328, (0 missing)
##       age               < 62.5   to the left,  improve=4.441278, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.580749, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.765354, (0 missing)
##   Surrogate splits:
##       bucket2008    < 2.5    to the right, agree=0.861, adj=0.717, (0 split)
##       kidney        < 0.5    to the right, agree=0.637, adj=0.262, (0 split)
##       heart.failure < 0.5    to the right, agree=0.608, adj=0.204, (0 split)
##       copd          < 0.5    to the right, agree=0.590, adj=0.166, (0 split)
##       alzheimers    < 0.5    to the right, agree=0.558, adj=0.102, (0 split)
## 
## Node number 453: 962 observations,    complexity param=0.0001051618
##   predicted class=B1  expected loss=0.5592516  P(node) =0.00350069
##     class counts:   424   364   114    55     5
##    probabilities: 0.441 0.378 0.119 0.057 0.005 
##   left son=906 (857 obs) right son=907 (105 obs)
##   Primary splits:
##       stroke            < 0.5    to the left,  improve=3.576264, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.114700, (0 missing)
##       reimbursement2008 < 59635  to the left,  improve=2.145451, (0 missing)
##       age               < 97.5   to the right, improve=1.742305, (0 missing)
##       copd              < 0.5    to the left,  improve=1.012750, (0 missing)
## 
## Node number 454: 1518 observations
##   predicted class=B1  expected loss=0.5685112  P(node) =0.005523957
##     class counts:   655   520   243    92     8
##    probabilities: 0.431 0.343 0.160 0.061 0.005 
## 
## Node number 455: 706 observations,    complexity param=0.0001162314
##   predicted class=B2  expected loss=0.6232295  P(node) =0.002569113
##     class counts:   233   266   118    77    12
##    probabilities: 0.330 0.377 0.167 0.109 0.017 
##   left son=910 (696 obs) right son=911 (10 obs)
##   Primary splits:
##       reimbursement2008 < 3155   to the right, improve=3.301177, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.232296, (0 missing)
##       copd              < 0.5    to the left,  improve=2.330270, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.835216, (0 missing)
##       age               < 92.5   to the right, improve=1.805094, (0 missing)
## 
## Node number 456: 440 observations,    complexity param=5.313437e-05
##   predicted class=B2  expected loss=0.5636364  P(node) =0.001601147
##     class counts:   177   192    49    20     2
##    probabilities: 0.402 0.436 0.111 0.045 0.005 
##   left son=912 (58 obs) right son=913 (382 obs)
##   Primary splits:
##       reimbursement2008 < 3155   to the left,  improve=3.3827400, (0 missing)
##       age               < 80.5   to the right, improve=2.1481460, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.6300393, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.5745512, (0 missing)
##       depression        < 0.5    to the right, improve=0.5547491, (0 missing)
## 
## Node number 457: 919 observations
##   predicted class=B2  expected loss=0.5505985  P(node) =0.003344214
##     class counts:   290   413   154    56     6
##    probabilities: 0.316 0.449 0.168 0.061 0.007 
## 
## Node number 460: 412 observations
##   predicted class=B1  expected loss=0.4757282  P(node) =0.001499256
##     class counts:   216   120    42    30     4
##    probabilities: 0.524 0.291 0.102 0.073 0.010 
## 
## Node number 461: 302 observations,    complexity param=7.748763e-05
##   predicted class=B2  expected loss=0.6059603  P(node) =0.001098969
##     class counts:   102   119    49    31     1
##    probabilities: 0.338 0.394 0.162 0.103 0.003 
##   left son=922 (9 obs) right son=923 (293 obs)
##   Primary splits:
##       age               < 92.5   to the right, improve=2.5766490, (0 missing)
##       stroke            < 0.5    to the right, improve=1.8961920, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.0608030, (0 missing)
##       reimbursement2008 < 32980  to the right, improve=1.0319510, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.7951977, (0 missing)
## 
## Node number 462: 282 observations,    complexity param=6.088314e-05
##   predicted class=B2  expected loss=0.6631206  P(node) =0.00102619
##     class counts:    88    95    71    23     5
##    probabilities: 0.312 0.337 0.252 0.082 0.018 
##   left son=924 (220 obs) right son=925 (62 obs)
##   Primary splits:
##       reimbursement2008 < 27390  to the left,  improve=2.933452, (0 missing)
##       age               < 79.5   to the left,  improve=2.171675, (0 missing)
##       bucket2008        < 4.5    to the right, improve=1.933271, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.142914, (0 missing)
##       ihd               < 0.5    to the left,  improve=1.125301, (0 missing)
##   Surrogate splits:
##       bucket2008 < 3.5    to the left,  agree=0.816, adj=0.161, (0 split)
## 
## Node number 463: 162 observations
##   predicted class=B2  expected loss=0.5246914  P(node) =0.0005895132
##     class counts:    30    77    36    15     4
##    probabilities: 0.185 0.475 0.222 0.093 0.025 
## 
## Node number 472: 159 observations,    complexity param=6.167383e-05
##   predicted class=B1  expected loss=0.591195  P(node) =0.0005785963
##     class counts:    65    51    33     8     2
##    probabilities: 0.409 0.321 0.208 0.050 0.013 
##   left son=944 (76 obs) right son=945 (83 obs)
##   Primary splits:
##       reimbursement2008 < 11995  to the right, improve=3.4294220, (0 missing)
##       age               < 65     to the left,  improve=1.4674530, (0 missing)
##       copd              < 0.5    to the left,  improve=1.0021090, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.7722947, (0 missing)
##       bucket2008        < 3.5    to the left,  improve=0.4091195, (0 missing)
##   Surrogate splits:
##       bucket2008    < 3.5    to the right, agree=0.673, adj=0.316, (0 split)
##       alzheimers    < 0.5    to the right, agree=0.591, adj=0.145, (0 split)
##       copd          < 0.5    to the right, agree=0.572, adj=0.105, (0 split)
##       heart.failure < 0.5    to the right, agree=0.560, adj=0.079, (0 split)
##       age           < 85.5   to the right, agree=0.541, adj=0.039, (0 split)
## 
## Node number 473: 586 observations
##   predicted class=B2  expected loss=0.6075085  P(node) =0.002132437
##     class counts:   167   230   117    64     8
##    probabilities: 0.285 0.392 0.200 0.109 0.014 
## 
## Node number 474: 237 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.5738397  P(node) =0.000862436
##     class counts:    35   101    72    26     3
##    probabilities: 0.148 0.426 0.304 0.110 0.013 
##   left son=948 (126 obs) right son=949 (111 obs)
##   Primary splits:
##       copd              < 0.5    to the left,  improve=2.2728500, (0 missing)
##       reimbursement2008 < 18275  to the left,  improve=1.9530690, (0 missing)
##       bucket2008        < 3.5    to the left,  improve=1.1622440, (0 missing)
##       age               < 75.5   to the right, improve=1.0471110, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.8993424, (0 missing)
##   Surrogate splits:
##       age           < 86.5   to the left,  agree=0.599, adj=0.144, (0 split)
##       heart.failure < 0.5    to the left,  agree=0.586, adj=0.117, (0 split)
##       kidney        < 0.5    to the left,  agree=0.582, adj=0.108, (0 split)
##       depression    < 0.5    to the left,  agree=0.570, adj=0.081, (0 split)
##       stroke        < 0.5    to the left,  agree=0.565, adj=0.072, (0 split)
## 
## Node number 475: 72 observations
##   predicted class=B3  expected loss=0.6111111  P(node) =0.0002620059
##     class counts:    14    16    28    11     3
##    probabilities: 0.194 0.222 0.389 0.153 0.042 
## 
## Node number 478: 226 observations
##   predicted class=B2  expected loss=0.6238938  P(node) =0.0008224073
##     class counts:    40    85    74    26     1
##    probabilities: 0.177 0.376 0.327 0.115 0.004 
## 
## Node number 479: 71 observations
##   predicted class=B3  expected loss=0.5492958  P(node) =0.0002583669
##     class counts:    12    19    32     7     1
##    probabilities: 0.169 0.268 0.451 0.099 0.014 
## 
## Node number 480: 403 observations
##   predicted class=B1  expected loss=0.4243176  P(node) =0.001466505
##     class counts:   232    86    61    20     4
##    probabilities: 0.576 0.213 0.151 0.050 0.010 
## 
## Node number 481: 2214 observations,    complexity param=0.0001439056
##   predicted class=B1  expected loss=0.5442638  P(node) =0.008056681
##     class counts:  1009   798   290   107    10
##    probabilities: 0.456 0.360 0.131 0.048 0.005 
##   left son=962 (1636 obs) right son=963 (578 obs)
##   Primary splits:
##       osteoporosis < 0.5    to the left,  improve=6.829533, (0 missing)
##       depression   < 0.5    to the left,  improve=5.940363, (0 missing)
##       copd         < 0.5    to the left,  improve=3.544519, (0 missing)
##       alzheimers   < 0.5    to the left,  improve=2.874488, (0 missing)
##       age          < 57.5   to the left,  improve=2.182371, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 9185   to the left,  agree=0.742, adj=0.01, (0 split)
## 
## Node number 482: 5563 observations,    complexity param=0.000785946
##   predicted class=B1  expected loss=0.6002157  P(node) =0.02024359
##     class counts:  2224  2125   853   328    33
##    probabilities: 0.400 0.382 0.153 0.059 0.006 
##   left son=964 (1363 obs) right son=965 (4200 obs)
##   Primary splits:
##       reimbursement2008 < 8955   to the right, improve=10.881900, (0 missing)
##       heart.failure     < 0.5    to the left,  improve= 9.391652, (0 missing)
##       copd              < 0.5    to the left,  improve= 8.088751, (0 missing)
##       bucket2008        < 2.5    to the right, improve= 7.479912, (0 missing)
##       age               < 31.5   to the left,  improve= 2.090877, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.959, adj=0.834, (0 split)
##       age        < 28.5   to the left,  agree=0.755, adj=0.001, (0 split)
## 
## Node number 483: 4392 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.5778689  P(node) =0.01598236
##     class counts:  1379  1854   796   336    27
##    probabilities: 0.314 0.422 0.181 0.077 0.006 
##   left son=966 (2928 obs) right son=967 (1464 obs)
##   Primary splits:
##       reimbursement2008 < 8325   to the left,  improve=7.509791, (0 missing)
##       copd              < 0.5    to the left,  improve=6.714633, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=5.287260, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=5.126640, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=3.940785, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.979, adj=0.936, (0 split)
## 
## Node number 488: 1063 observations,    complexity param=6.918538e-05
##   predicted class=B2  expected loss=0.5983067  P(node) =0.003868226
##     class counts:   336   427   192    95    13
##    probabilities: 0.316 0.402 0.181 0.089 0.012 
##   left son=976 (102 obs) right son=977 (961 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=6.866573, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=4.371067, (0 missing)
##       copd              < 0.5    to the left,  improve=3.836869, (0 missing)
##       reimbursement2008 < 18130  to the left,  improve=3.632764, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=2.933358, (0 missing)
## 
## Node number 489: 3242 observations
##   predicted class=B2  expected loss=0.5009254  P(node) =0.01179754
##     class counts:   813  1618   554   233    24
##    probabilities: 0.251 0.499 0.171 0.072 0.007 
## 
## Node number 496: 964 observations,    complexity param=0.0001411382
##   predicted class=B1  expected loss=0.6307054  P(node) =0.003507968
##     class counts:   356   348   167    82    11
##    probabilities: 0.369 0.361 0.173 0.085 0.011 
##   left son=992 (572 obs) right son=993 (392 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=5.116701, (0 missing)
##       reimbursement2008 < 8255   to the left,  improve=4.417859, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=3.940989, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.162605, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=3.038248, (0 missing)
##   Surrogate splits:
##       stroke            < 0.5    to the left,  agree=0.601, adj=0.018, (0 split)
##       reimbursement2008 < 14855  to the left,  agree=0.598, adj=0.010, (0 split)
##       copd              < 0.5    to the left,  agree=0.594, adj=0.003, (0 split)
## 
## Node number 497: 6822 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.6002639  P(node) =0.02482506
##     class counts:  1626  2727  1500   847   122
##    probabilities: 0.238 0.400 0.220 0.124 0.018 
##   left son=994 (3172 obs) right son=995 (3650 obs)
##   Primary splits:
##       reimbursement2008 < 6325   to the left,  improve=11.481700, (0 missing)
##       depression        < 0.5    to the left,  improve=11.166950, (0 missing)
##       bucket2008        < 2.5    to the left,  improve= 7.602059, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve= 6.404955, (0 missing)
##       copd              < 0.5    to the left,  improve= 5.945024, (0 missing)
##   Surrogate splits:
##       bucket2008    < 2.5    to the left,  agree=0.869, adj=0.717, (0 split)
##       copd          < 0.5    to the left,  agree=0.576, adj=0.088, (0 split)
##       heart.failure < 0.5    to the left,  agree=0.570, adj=0.076, (0 split)
##       alzheimers    < 0.5    to the left,  agree=0.545, adj=0.020, (0 split)
##       age           < 31.5   to the left,  agree=0.536, adj=0.003, (0 split)
## 
## Node number 504: 1291 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5530596  P(node) =0.004697911
##     class counts:   183   577   280   219    32
##    probabilities: 0.142 0.447 0.217 0.170 0.025 
##   left son=1008 (973 obs) right son=1009 (318 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=5.737334, (0 missing)
##       reimbursement2008 < 32055  to the left,  improve=1.892346, (0 missing)
##       age               < 84.5   to the right, improve=1.763761, (0 missing)
##       ihd               < 0.5    to the left,  improve=1.710826, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.100420, (0 missing)
##   Surrogate splits:
##       age < 28.5   to the right, agree=0.755, adj=0.006, (0 split)
## 
## Node number 505: 2054 observations
##   predicted class=B2  expected loss=0.5978578  P(node) =0.007474445
##     class counts:   189   826   557   413    69
##    probabilities: 0.092 0.402 0.271 0.201 0.034 
## 
## Node number 506: 520 observations
##   predicted class=B2  expected loss=0.5769231  P(node) =0.001892265
##     class counts:    50   220   120   108    22
##    probabilities: 0.096 0.423 0.231 0.208 0.042 
## 
## Node number 507: 1537 observations,    complexity param=7.748763e-05
##   predicted class=B2  expected loss=0.6363045  P(node) =0.005593098
##     class counts:    87   559   353   446    92
##    probabilities: 0.057 0.364 0.230 0.290 0.060 
##   left son=1014 (1286 obs) right son=1015 (251 obs)
##   Primary splits:
##       age               < 62.5   to the right, improve=4.292573, (0 missing)
##       reimbursement2008 < 43950  to the left,  improve=3.358938, (0 missing)
##       cancer            < 0.5    to the right, improve=2.803709, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.956332, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.605851, (0 missing)
## 
## Node number 508: 2489 observations,    complexity param=0.0002036818
##   predicted class=B2  expected loss=0.7219767  P(node) =0.009057397
##     class counts:   541   692   436   670   150
##    probabilities: 0.217 0.278 0.175 0.269 0.060 
##   left son=1016 (1317 obs) right son=1017 (1172 obs)
##   Primary splits:
##       copd              < 0.5    to the right, improve=9.293163, (0 missing)
##       reimbursement2008 < 23175  to the left,  improve=7.265866, (0 missing)
##       ihd               < 0.5    to the left,  improve=7.177016, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=4.187307, (0 missing)
##       bucket2008        < 4.5    to the left,  improve=3.684093, (0 missing)
##   Surrogate splits:
##       heart.failure     < 0.5    to the right, agree=0.575, adj=0.097, (0 split)
##       alzheimers        < 0.5    to the right, agree=0.556, adj=0.057, (0 split)
##       ihd               < 0.5    to the right, agree=0.555, adj=0.055, (0 split)
##       reimbursement2008 < 27380  to the right, agree=0.544, adj=0.032, (0 split)
##       age               < 52.5   to the right, agree=0.532, adj=0.005, (0 split)
## 
## Node number 509: 2809 observations
##   predicted class=B2  expected loss=0.6600214  P(node) =0.01022187
##     class counts:   367   955   615   694   178
##    probabilities: 0.131 0.340 0.219 0.247 0.063 
## 
## Node number 812: 95 observations
##   predicted class=B1  expected loss=0.4315789  P(node) =0.0003457022
##     class counts:    54    25    11     5     0
##    probabilities: 0.568 0.263 0.116 0.053 0.000 
## 
## Node number 813: 33 observations
##   predicted class=B2  expected loss=0.4848485  P(node) =0.000120086
##     class counts:    10    17     3     3     0
##    probabilities: 0.303 0.515 0.091 0.091 0.000 
## 
## Node number 826: 596 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5536913  P(node) =0.002168826
##     class counts:   266   214    77    34     5
##    probabilities: 0.446 0.359 0.129 0.057 0.008 
##   left son=1652 (555 obs) right son=1653 (41 obs)
##   Primary splits:
##       reimbursement2008 < 1765   to the right, improve=2.4482060, (0 missing)
##       age               < 81.5   to the right, improve=2.3548750, (0 missing)
##       ihd               < 0.5    to the left,  improve=2.3213280, (0 missing)
##       cancer            < 0.5    to the left,  improve=2.1512770, (0 missing)
##       depression        < 0.5    to the left,  improve=0.5755867, (0 missing)
## 
## Node number 827: 38 observations
##   predicted class=B2  expected loss=0.5526316  P(node) =0.0001382809
##     class counts:     7    17     6     8     0
##    probabilities: 0.184 0.447 0.158 0.211 0.000 
## 
## Node number 846: 22 observations
##   predicted class=B1  expected loss=0.4545455  P(node) =8.005735e-05
##     class counts:    12     5     4     1     0
##    probabilities: 0.545 0.227 0.182 0.045 0.000 
## 
## Node number 847: 96 observations
##   predicted class=B2  expected loss=0.4791667  P(node) =0.0003493412
##     class counts:    29    50    12     5     0
##    probabilities: 0.302 0.521 0.125 0.052 0.000 
## 
## Node number 848: 385 observations,    complexity param=8.855729e-05
##   predicted class=B1  expected loss=0.5454545  P(node) =0.001401004
##     class counts:   175   146    39    23     2
##    probabilities: 0.455 0.379 0.101 0.060 0.005 
##   left son=1696 (263 obs) right son=1697 (122 obs)
##   Primary splits:
##       age               < 80.5   to the left,  improve=2.5496070, (0 missing)
##       ihd               < 0.5    to the left,  improve=1.7465940, (0 missing)
##       reimbursement2008 < 3025   to the right, improve=0.9395484, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.7038989, (0 missing)
##       depression        < 0.5    to the left,  improve=0.3133237, (0 missing)
## 
## Node number 849: 110 observations
##   predicted class=B2  expected loss=0.4909091  P(node) =0.0004002868
##     class counts:    40    56    11     3     0
##    probabilities: 0.364 0.509 0.100 0.027 0.000 
## 
## Node number 866: 1499 observations
##   predicted class=B1  expected loss=0.490994  P(node) =0.005454817
##     class counts:   763   492   189    52     3
##    probabilities: 0.509 0.328 0.126 0.035 0.002 
## 
## Node number 867: 1228 observations,    complexity param=6.918538e-05
##   predicted class=B1  expected loss=0.5537459  P(node) =0.004468656
##     class counts:   548   456   161    57     6
##    probabilities: 0.446 0.371 0.131 0.046 0.005 
##   left son=1734 (171 obs) right son=1735 (1057 obs)
##   Primary splits:
##       reimbursement2008 < 2995   to the right, improve=3.399761, (0 missing)
##       bucket2008        < 1.5    to the right, improve=3.399761, (0 missing)
##       age               < 83.5   to the left,  improve=2.697325, (0 missing)
##       kidney            < 0.5    to the left,  improve=2.465389, (0 missing)
##       depression        < 0.5    to the left,  improve=1.722272, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the right, agree=1, adj=1, (0 split)
## 
## Node number 868: 167 observations
##   predicted class=B1  expected loss=0.502994  P(node) =0.0006077081
##     class counts:    83    50    25     7     2
##    probabilities: 0.497 0.299 0.150 0.042 0.012 
## 
## Node number 869: 71 observations
##   predicted class=B2  expected loss=0.4929577  P(node) =0.0002583669
##     class counts:    19    36    13     2     1
##    probabilities: 0.268 0.507 0.183 0.028 0.014 
## 
## Node number 874: 393 observations
##   predicted class=B1  expected loss=0.5139949  P(node) =0.001430115
##     class counts:   191   134    46    20     2
##    probabilities: 0.486 0.341 0.117 0.051 0.005 
## 
## Node number 875: 1791 observations,    complexity param=0.0001129105
##   predicted class=B1  expected loss=0.6136237  P(node) =0.006517396
##     class counts:   692   650   300   137    12
##    probabilities: 0.386 0.363 0.168 0.076 0.007 
##   left son=1750 (1752 obs) right son=1751 (39 obs)
##   Primary splits:
##       age               < 39.5   to the right, improve=3.631907, (0 missing)
##       depression        < 0.5    to the left,  improve=3.598994, (0 missing)
##       reimbursement2008 < 2475   to the left,  improve=1.828499, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.790378, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.609073, (0 missing)
## 
## Node number 876: 180 observations,    complexity param=8.302246e-05
##   predicted class=B1  expected loss=0.5666667  P(node) =0.0006550147
##     class counts:    78    68    23    11     0
##    probabilities: 0.433 0.378 0.128 0.061 0.000 
##   left son=1752 (112 obs) right son=1753 (68 obs)
##   Primary splits:
##       reimbursement2008 < 2455   to the left,  improve=3.4787820, (0 missing)
##       age               < 88.5   to the left,  improve=1.3486290, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.8074074, (0 missing)
##       copd              < 0.5    to the left,  improve=0.6952328, (0 missing)
##       ihd               < 0.5    to the right, improve=0.3305556, (0 missing)
## 
## Node number 877: 433 observations,    complexity param=6.272808e-05
##   predicted class=B2  expected loss=0.6351039  P(node) =0.001575674
##     class counts:   156   158    88    25     6
##    probabilities: 0.360 0.365 0.203 0.058 0.014 
##   left son=1754 (403 obs) right son=1755 (30 obs)
##   Primary splits:
##       stroke            < 0.5    to the left,  improve=1.8988510, (0 missing)
##       age               < 45.5   to the left,  improve=1.3010460, (0 missing)
##       reimbursement2008 < 2255   to the left,  improve=1.2799960, (0 missing)
##       depression        < 0.5    to the left,  improve=1.2338070, (0 missing)
##       cancer            < 0.5    to the right, improve=0.6525541, (0 missing)
## 
## Node number 882: 25 observations
##   predicted class=B2  expected loss=0.24  P(node) =9.097426e-05
##     class counts:     6    19     0     0     0
##    probabilities: 0.240 0.760 0.000 0.000 0.000 
## 
## Node number 883: 349 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5673352  P(node) =0.001270001
##     class counts:   151   135    46    16     1
##    probabilities: 0.433 0.387 0.132 0.046 0.003 
##   left son=1766 (336 obs) right son=1767 (13 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=2.2574550, (0 missing)
##       age               < 69.5   to the left,  improve=1.5047970, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.0520090, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.8771249, (0 missing)
##       reimbursement2008 < 2535   to the right, improve=0.8193194, (0 missing)
## 
## Node number 886: 1101 observations,    complexity param=7.748763e-05
##   predicted class=B1  expected loss=0.595822  P(node) =0.004006506
##     class counts:   445   444   150    57     5
##    probabilities: 0.404 0.403 0.136 0.052 0.005 
##   left son=1772 (1057 obs) right son=1773 (44 obs)
##   Primary splits:
##       stroke            < 0.5    to the left,  improve=2.0669290, (0 missing)
##       age               < 46.5   to the left,  improve=1.0570040, (0 missing)
##       reimbursement2008 < 2535   to the right, improve=0.9632398, (0 missing)
##       cancer            < 0.5    to the left,  improve=0.9197684, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.7998904, (0 missing)
## 
## Node number 887: 432 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.587963  P(node) =0.001572035
##     class counts:   143   178    73    34     4
##    probabilities: 0.331 0.412 0.169 0.079 0.009 
##   left son=1774 (403 obs) right son=1775 (29 obs)
##   Primary splits:
##       reimbursement2008 < 2215   to the right, improve=1.979831, (0 missing)
##       depression        < 0.5    to the left,  improve=1.399095, (0 missing)
##       age               < 65.5   to the right, improve=1.336452, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.190308, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.062301, (0 missing)
## 
## Node number 900: 117 observations
##   predicted class=B1  expected loss=0.3418803  P(node) =0.0004257595
##     class counts:    77    25     8     7     0
##    probabilities: 0.658 0.214 0.068 0.060 0.000 
## 
## Node number 901: 693 observations,    complexity param=5.258089e-05
##   predicted class=B1  expected loss=0.5165945  P(node) =0.002521807
##     class counts:   335   232   100    26     0
##    probabilities: 0.483 0.335 0.144 0.038 0.000 
##   left son=1802 (684 obs) right son=1803 (9 obs)
##   Primary splits:
##       reimbursement2008 < 11105  to the left,  improve=2.2165640, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.2536740, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.8354877, (0 missing)
##       age               < 34     to the left,  improve=0.8168384, (0 missing)
##       copd              < 0.5    to the left,  improve=0.3325841, (0 missing)
## 
## Node number 904: 1626 observations
##   predicted class=B1  expected loss=0.4391144  P(node) =0.005916966
##     class counts:   912   414   190    99    11
##    probabilities: 0.561 0.255 0.117 0.061 0.007 
## 
## Node number 905: 1678 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.511323  P(node) =0.006106192
##     class counts:   820   565   199    84    10
##    probabilities: 0.489 0.337 0.119 0.050 0.006 
##   left son=1810 (1608 obs) right son=1811 (70 obs)
##   Primary splits:
##       reimbursement2008 < 5695   to the left,  improve=3.4228850, (0 missing)
##       age               < 54.5   to the left,  improve=3.4011680, (0 missing)
##       kidney            < 0.5    to the left,  improve=2.7930360, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.6256038, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.4872841, (0 missing)
## 
## Node number 906: 857 observations,    complexity param=8.855729e-05
##   predicted class=B1  expected loss=0.5425904  P(node) =0.003118598
##     class counts:   392   313   100    48     4
##    probabilities: 0.457 0.365 0.117 0.056 0.005 
##   left son=1812 (405 obs) right son=1813 (452 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the left,  improve=1.8446560, (0 missing)
##       reimbursement2008 < 8165   to the right, improve=1.5511950, (0 missing)
##       age               < 58.5   to the right, improve=1.3750490, (0 missing)
##       bucket2008        < 2.5    to the right, improve=1.0178390, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.9078092, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 7010   to the left,  agree=0.629, adj=0.215, (0 split)
##       bucket2008        < 2.5    to the left,  agree=0.611, adj=0.178, (0 split)
##       kidney            < 0.5    to the left,  agree=0.585, adj=0.121, (0 split)
##       copd              < 0.5    to the left,  agree=0.583, adj=0.119, (0 split)
##       age               < 77.5   to the left,  agree=0.555, adj=0.059, (0 split)
## 
## Node number 907: 105 observations
##   predicted class=B2  expected loss=0.5142857  P(node) =0.0003820919
##     class counts:    32    51    14     7     1
##    probabilities: 0.305 0.486 0.133 0.067 0.010 
## 
## Node number 910: 696 observations,    complexity param=0.0001162314
##   predicted class=B2  expected loss=0.6192529  P(node) =0.002532723
##     class counts:   232   265   112    75    12
##    probabilities: 0.333 0.381 0.161 0.108 0.017 
##   left son=1820 (177 obs) right son=1821 (519 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the left,  improve=3.566495, (0 missing)
##       copd              < 0.5    to the left,  improve=2.688595, (0 missing)
##       age               < 70.5   to the right, improve=1.873250, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.737201, (0 missing)
##       reimbursement2008 < 7405   to the left,  improve=1.699902, (0 missing)
## 
## Node number 911: 10 observations
##   predicted class=B3  expected loss=0.4  P(node) =3.63897e-05
##     class counts:     1     1     6     2     0
##    probabilities: 0.100 0.100 0.600 0.200 0.000 
## 
## Node number 912: 58 observations
##   predicted class=B2  expected loss=0.3793103  P(node) =0.0002110603
##     class counts:    20    36     0     1     1
##    probabilities: 0.345 0.621 0.000 0.017 0.017 
## 
## Node number 913: 382 observations,    complexity param=5.313437e-05
##   predicted class=B1  expected loss=0.5890052  P(node) =0.001390087
##     class counts:   157   156    49    19     1
##    probabilities: 0.411 0.408 0.128 0.050 0.003 
##   left son=1826 (25 obs) right son=1827 (357 obs)
##   Primary splits:
##       reimbursement2008 < 3245   to the left,  improve=2.6514540, (0 missing)
##       age               < 80.5   to the right, improve=2.1655330, (0 missing)
##       depression        < 0.5    to the left,  improve=1.2095670, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.1024610, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.7876318, (0 missing)
## 
## Node number 922: 9 observations
##   predicted class=B1  expected loss=0.3333333  P(node) =3.275073e-05
##     class counts:     6     0     1     1     1
##    probabilities: 0.667 0.000 0.111 0.111 0.111 
## 
## Node number 923: 293 observations,    complexity param=7.748763e-05
##   predicted class=B2  expected loss=0.5938567  P(node) =0.001066218
##     class counts:    96   119    48    30     0
##    probabilities: 0.328 0.406 0.164 0.102 0.000 
##   left son=1846 (39 obs) right son=1847 (254 obs)
##   Primary splits:
##       stroke            < 0.5    to the right, improve=2.1766940, (0 missing)
##       age               < 65.5   to the right, improve=1.5636490, (0 missing)
##       reimbursement2008 < 11660  to the right, improve=1.0372370, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.9901623, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.7188410, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 79760  to the right, agree=0.87, adj=0.026, (0 split)
## 
## Node number 924: 220 observations,    complexity param=6.088314e-05
##   predicted class=B1  expected loss=0.65  P(node) =0.0008005735
##     class counts:    77    66    57    16     4
##    probabilities: 0.350 0.300 0.259 0.073 0.018 
##   left son=1848 (132 obs) right son=1849 (88 obs)
##   Primary splits:
##       reimbursement2008 < 12810  to the right, improve=2.5787880, (0 missing)
##       age               < 76.5   to the left,  improve=2.1718510, (0 missing)
##       bucket2008        < 3.5    to the right, improve=1.0384950, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.8392769, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.7969697, (0 missing)
##   Surrogate splits:
##       bucket2008 < 3.5    to the right, agree=0.636, adj=0.091, (0 split)
## 
## Node number 925: 62 observations
##   predicted class=B2  expected loss=0.5322581  P(node) =0.0002256162
##     class counts:    11    29    14     7     1
##    probabilities: 0.177 0.468 0.226 0.113 0.016 
## 
## Node number 944: 76 observations
##   predicted class=B1  expected loss=0.4736842  P(node) =0.0002765618
##     class counts:    40    18    12     5     1
##    probabilities: 0.526 0.237 0.158 0.066 0.013 
## 
## Node number 945: 83 observations
##   predicted class=B2  expected loss=0.6024096  P(node) =0.0003020345
##     class counts:    25    33    21     3     1
##    probabilities: 0.301 0.398 0.253 0.036 0.012 
## 
## Node number 948: 126 observations
##   predicted class=B2  expected loss=0.5079365  P(node) =0.0004585103
##     class counts:    22    62    33     9     0
##    probabilities: 0.175 0.492 0.262 0.071 0.000 
## 
## Node number 949: 111 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.6486486  P(node) =0.0004039257
##     class counts:    13    39    39    17     3
##    probabilities: 0.117 0.351 0.351 0.153 0.027 
##   left son=1898 (54 obs) right son=1899 (57 obs)
##   Primary splits:
##       age               < 75.5   to the left,  improve=1.9702330, (0 missing)
##       reimbursement2008 < 23940  to the left,  improve=1.5183950, (0 missing)
##       stroke            < 0.5    to the right, improve=1.4096100, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=1.1218360, (0 missing)
##       kidney            < 0.5    to the right, improve=0.9749865, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 36125  to the right, agree=0.586, adj=0.148, (0 split)
##       depression        < 0.5    to the right, agree=0.568, adj=0.111, (0 split)
##       alzheimers        < 0.5    to the right, agree=0.550, adj=0.074, (0 split)
##       bucket2008        < 4.5    to the right, agree=0.532, adj=0.037, (0 split)
## 
## Node number 962: 1636 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5177262  P(node) =0.005953356
##     class counts:   789   562   198    80     7
##    probabilities: 0.482 0.344 0.121 0.049 0.004 
##   left son=1924 (1127 obs) right son=1925 (509 obs)
##   Primary splits:
##       alzheimers        < 0.5    to the left,  improve=2.275005, (0 missing)
##       depression        < 0.5    to the left,  improve=2.200834, (0 missing)
##       age               < 56.5   to the right, improve=2.161392, (0 missing)
##       reimbursement2008 < 3635   to the left,  improve=1.571205, (0 missing)
##       copd              < 0.5    to the left,  improve=1.483908, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 9210   to the left,  agree=0.69, adj=0.004, (0 split)
## 
## Node number 963: 578 observations,    complexity param=0.0001439056
##   predicted class=B2  expected loss=0.5916955  P(node) =0.002103325
##     class counts:   220   236    92    27     3
##    probabilities: 0.381 0.408 0.159 0.047 0.005 
##   left son=1926 (339 obs) right son=1927 (239 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=4.643194, (0 missing)
##       copd              < 0.5    to the left,  improve=3.299852, (0 missing)
##       reimbursement2008 < 4535   to the left,  improve=1.771265, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.392171, (0 missing)
##       age               < 39.5   to the right, improve=1.271983, (0 missing)
##   Surrogate splits:
##       age < 43.5   to the right, agree=0.602, adj=0.038, (0 split)
## 
## Node number 964: 1363 observations,    complexity param=6.088314e-05
##   predicted class=B1  expected loss=0.5561262  P(node) =0.004959917
##     class counts:   605   435   219    92    12
##    probabilities: 0.444 0.319 0.161 0.067 0.009 
##   left son=1928 (798 obs) right son=1929 (565 obs)
##   Primary splits:
##       copd              < 0.5    to the left,  improve=7.963265, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=4.055475, (0 missing)
##       reimbursement2008 < 29005  to the left,  improve=3.506334, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.818093, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.815648, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 55265  to the left,  agree=0.589, adj=0.009, (0 split)
##       bucket2008        < 4.5    to the left,  agree=0.589, adj=0.009, (0 split)
##       age               < 27.5   to the right, agree=0.588, adj=0.005, (0 split)
## 
## Node number 965: 4200 observations,    complexity param=0.0004649258
##   predicted class=B2  expected loss=0.597619  P(node) =0.01528368
##     class counts:  1619  1690   634   236    21
##    probabilities: 0.385 0.402 0.151 0.056 0.005 
##   left son=1930 (1953 obs) right son=1931 (2247 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the left,  improve=7.153470, (0 missing)
##       copd              < 0.5    to the left,  improve=3.796544, (0 missing)
##       age               < 49.5   to the left,  improve=2.763159, (0 missing)
##       reimbursement2008 < 3415   to the left,  improve=2.562311, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.867356, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 4495   to the left,  agree=0.563, adj=0.060, (0 split)
##       copd              < 0.5    to the left,  agree=0.551, adj=0.035, (0 split)
##       alzheimers        < 0.5    to the left,  agree=0.540, adj=0.010, (0 split)
##       age               < 45.5   to the left,  agree=0.536, adj=0.002, (0 split)
## 
## Node number 966: 2928 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5529372  P(node) =0.01065491
##     class counts:   904  1309   506   190    19
##    probabilities: 0.309 0.447 0.173 0.065 0.006 
##   left son=1932 (1987 obs) right son=1933 (941 obs)
##   Primary splits:
##       copd              < 0.5    to the left,  improve=5.088653, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=4.205973, (0 missing)
##       reimbursement2008 < 8045   to the left,  improve=3.909055, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.356901, (0 missing)
##       stroke            < 0.5    to the left,  improve=3.071040, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 8235   to the left,  agree=0.68, adj=0.005, (0 split)
## 
## Node number 967: 1464 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.6277322  P(node) =0.005327453
##     class counts:   475   545   290   146     8
##    probabilities: 0.324 0.372 0.198 0.100 0.005 
##   left son=1934 (36 obs) right son=1935 (1428 obs)
##   Primary splits:
##       reimbursement2008 < 8485   to the left,  improve=3.191750, (0 missing)
##       age               < 78.5   to the left,  improve=2.281932, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=2.180745, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.944689, (0 missing)
##       copd              < 0.5    to the left,  improve=1.512341, (0 missing)
## 
## Node number 976: 102 observations
##   predicted class=B1  expected loss=0.4803922  P(node) =0.000371175
##     class counts:    53    28    13     7     1
##    probabilities: 0.520 0.275 0.127 0.069 0.010 
## 
## Node number 977: 961 observations
##   predicted class=B2  expected loss=0.5848075  P(node) =0.003497051
##     class counts:   283   399   179    88    12
##    probabilities: 0.294 0.415 0.186 0.092 0.012 
## 
## Node number 992: 572 observations,    complexity param=0.0001411382
##   predicted class=B1  expected loss=0.5909091  P(node) =0.002081491
##     class counts:   234   183    92    57     6
##    probabilities: 0.409 0.320 0.161 0.100 0.010 
##   left son=1984 (101 obs) right son=1985 (471 obs)
##   Primary splits:
##       reimbursement2008 < 3545   to the left,  improve=4.251847, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=2.618377, (0 missing)
##       age               < 69.5   to the right, improve=2.566628, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.664728, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.532473, (0 missing)
## 
## Node number 993: 392 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.5790816  P(node) =0.001426476
##     class counts:   122   165    75    25     5
##    probabilities: 0.311 0.421 0.191 0.064 0.013 
##   left son=1986 (9 obs) right son=1987 (383 obs)
##   Primary splits:
##       reimbursement2008 < 14460  to the right, improve=2.744913, (0 missing)
##       age               < 48.5   to the left,  improve=1.556717, (0 missing)
##       copd              < 0.5    to the left,  improve=1.522824, (0 missing)
##       bucket2008        < 2.5    to the right, improve=1.319075, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.292043, (0 missing)
## 
## Node number 994: 3172 observations
##   predicted class=B2  expected loss=0.5630517  P(node) =0.01154281
##     class counts:   709  1386   688   337    52
##    probabilities: 0.224 0.437 0.217 0.106 0.016 
## 
## Node number 995: 3650 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.6326027  P(node) =0.01328224
##     class counts:   917  1341   812   510    70
##    probabilities: 0.251 0.367 0.222 0.140 0.019 
##   left son=1990 (2424 obs) right son=1991 (1226 obs)
##   Primary splits:
##       osteoporosis  < 0.5    to the left,  improve=8.187206, (0 missing)
##       depression    < 0.5    to the left,  improve=5.387571, (0 missing)
##       copd          < 0.5    to the left,  improve=5.189054, (0 missing)
##       alzheimers    < 0.5    to the left,  improve=3.710849, (0 missing)
##       heart.failure < 0.5    to the left,  improve=2.971629, (0 missing)
## 
## Node number 1008: 973 observations
##   predicted class=B2  expected loss=0.5611511  P(node) =0.003540718
##     class counts:   160   427   184   174    28
##    probabilities: 0.164 0.439 0.189 0.179 0.029 
## 
## Node number 1009: 318 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5283019  P(node) =0.001157193
##     class counts:    23   150    96    45     4
##    probabilities: 0.072 0.472 0.302 0.142 0.013 
##   left son=2018 (293 obs) right son=2019 (25 obs)
##   Primary splits:
##       reimbursement2008 < 16525  to the right, improve=7.797134, (0 missing)
##       ihd               < 0.5    to the left,  improve=3.194748, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.170376, (0 missing)
##       bucket2008        < 3.5    to the right, improve=1.159119, (0 missing)
##       age               < 55     to the right, improve=1.113448, (0 missing)
## 
## Node number 1014: 1286 observations
##   predicted class=B2  expected loss=0.6251944  P(node) =0.004679716
##     class counts:    74   482   303   348    79
##    probabilities: 0.058 0.375 0.236 0.271 0.061 
## 
## Node number 1015: 251 observations,    complexity param=6.088314e-05
##   predicted class=B4  expected loss=0.6095618  P(node) =0.0009133816
##     class counts:    13    77    50    98    13
##    probabilities: 0.052 0.307 0.199 0.390 0.052 
##   left son=2030 (237 obs) right son=2031 (14 obs)
##   Primary splits:
##       reimbursement2008 < 101585 to the left,  improve=3.425401, (0 missing)
##       age               < 61.5   to the left,  improve=2.440583, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=2.158559, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=2.020557, (0 missing)
##       cancer            < 0.5    to the right, improve=1.778561, (0 missing)
## 
## Node number 1016: 1317 observations,    complexity param=0.0001439056
##   predicted class=B2  expected loss=0.6757783  P(node) =0.004792524
##     class counts:   269   427   234   313    74
##    probabilities: 0.204 0.324 0.178 0.238 0.056 
##   left son=2032 (72 obs) right son=2033 (1245 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=5.587185, (0 missing)
##       reimbursement2008 < 22435  to the left,  improve=5.039175, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=3.150989, (0 missing)
##       age               < 50.5   to the right, improve=2.709964, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=2.161876, (0 missing)
## 
## Node number 1017: 1172 observations,    complexity param=0.0002036818
##   predicted class=B4  expected loss=0.6953925  P(node) =0.004264873
##     class counts:   272   265   202   357    76
##    probabilities: 0.232 0.226 0.172 0.305 0.065 
##   left son=2034 (191 obs) right son=2035 (981 obs)
##   Primary splits:
##       reimbursement2008 < 43640  to the right, improve=6.105110, (0 missing)
##       bucket2008        < 4.5    to the right, improve=4.295055, (0 missing)
##       ihd               < 0.5    to the left,  improve=2.740224, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=2.395917, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.864237, (0 missing)
##   Surrogate splits:
##       bucket2008 < 4.5    to the right, agree=0.925, adj=0.539, (0 split)
## 
## Node number 1652: 555 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5441441  P(node) =0.002019629
##     class counts:   253   193    76    29     4
##    probabilities: 0.456 0.348 0.137 0.052 0.007 
##   left son=3304 (265 obs) right son=3305 (290 obs)
##   Primary splits:
##       reimbursement2008 < 1955   to the left,  improve=2.2492970, (0 missing)
##       age               < 44.5   to the right, improve=2.2010530, (0 missing)
##       cancer            < 0.5    to the left,  improve=2.0271900, (0 missing)
##       ihd               < 0.5    to the left,  improve=2.0227740, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.6965966, (0 missing)
##   Surrogate splits:
##       ihd    < 0.5    to the left,  agree=0.539, adj=0.034, (0 split)
##       age    < 90.5   to the right, agree=0.532, adj=0.019, (0 split)
##       cancer < 0.5    to the right, agree=0.528, adj=0.011, (0 split)
## 
## Node number 1653: 41 observations
##   predicted class=B2  expected loss=0.4878049  P(node) =0.0001491978
##     class counts:    13    21     1     5     1
##    probabilities: 0.317 0.512 0.024 0.122 0.024 
## 
## Node number 1696: 263 observations
##   predicted class=B1  expected loss=0.4980989  P(node) =0.0009570492
##     class counts:   132    94    26    11     0
##    probabilities: 0.502 0.357 0.099 0.042 0.000 
## 
## Node number 1697: 122 observations
##   predicted class=B2  expected loss=0.5737705  P(node) =0.0004439544
##     class counts:    43    52    13    12     2
##    probabilities: 0.352 0.426 0.107 0.098 0.016 
## 
## Node number 1734: 171 observations
##   predicted class=B1  expected loss=0.4736842  P(node) =0.0006222639
##     class counts:    90    46    24    10     1
##    probabilities: 0.526 0.269 0.140 0.058 0.006 
## 
## Node number 1735: 1057 observations,    complexity param=6.918538e-05
##   predicted class=B1  expected loss=0.5666982  P(node) =0.003846392
##     class counts:   458   410   137    47     5
##    probabilities: 0.433 0.388 0.130 0.044 0.005 
##   left son=3470 (840 obs) right son=3471 (217 obs)
##   Primary splits:
##       age               < 83.5   to the left,  improve=3.809819, (0 missing)
##       kidney            < 0.5    to the left,  improve=2.564065, (0 missing)
##       depression        < 0.5    to the left,  improve=1.351420, (0 missing)
##       copd              < 0.5    to the left,  improve=1.145117, (0 missing)
##       reimbursement2008 < 2975   to the left,  improve=1.026292, (0 missing)
## 
## Node number 1750: 1752 observations,    complexity param=0.0001129105
##   predicted class=B1  expected loss=0.6084475  P(node) =0.006375476
##     class counts:   686   629   294   131    12
##    probabilities: 0.392 0.359 0.168 0.075 0.007 
##   left son=3500 (1099 obs) right son=3501 (653 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=3.008256, (0 missing)
##       age               < 97.5   to the left,  improve=2.167182, (0 missing)
##       reimbursement2008 < 3055   to the left,  improve=1.739250, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.536121, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.306511, (0 missing)
##   Surrogate splits:
##       age < 41.5   to the right, agree=0.628, adj=0.002, (0 split)
## 
## Node number 1751: 39 observations
##   predicted class=B2  expected loss=0.4615385  P(node) =0.0001419198
##     class counts:     6    21     6     6     0
##    probabilities: 0.154 0.538 0.154 0.154 0.000 
## 
## Node number 1752: 112 observations
##   predicted class=B1  expected loss=0.5  P(node) =0.0004075647
##     class counts:    56    33    14     9     0
##    probabilities: 0.500 0.295 0.125 0.080 0.000 
## 
## Node number 1753: 68 observations
##   predicted class=B2  expected loss=0.4852941  P(node) =0.00024745
##     class counts:    22    35     9     2     0
##    probabilities: 0.324 0.515 0.132 0.029 0.000 
## 
## Node number 1754: 403 observations,    complexity param=6.272808e-05
##   predicted class=B1  expected loss=0.6253102  P(node) =0.001466505
##     class counts:   151   147    79    20     6
##    probabilities: 0.375 0.365 0.196 0.050 0.015 
##   left son=3508 (382 obs) right son=3509 (21 obs)
##   Primary splits:
##       reimbursement2008 < 2585   to the left,  improve=1.3835870, (0 missing)
##       age               < 45.5   to the left,  improve=1.1912610, (0 missing)
##       depression        < 0.5    to the left,  improve=0.8974996, (0 missing)
##       cancer            < 0.5    to the right, improve=0.7248908, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.2961750, (0 missing)
## 
## Node number 1755: 30 observations
##   predicted class=B2  expected loss=0.6333333  P(node) =0.0001091691
##     class counts:     5    11     9     5     0
##    probabilities: 0.167 0.367 0.300 0.167 0.000 
## 
## Node number 1766: 336 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5535714  P(node) =0.001222694
##     class counts:   150   128    44    14     0
##    probabilities: 0.446 0.381 0.131 0.042 0.000 
##   left son=3532 (322 obs) right son=3533 (14 obs)
##   Primary splits:
##       age               < 90.5   to the left,  improve=1.4565220, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.1063780, (0 missing)
##       reimbursement2008 < 2325   to the right, improve=0.8683190, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.8476676, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.4098214, (0 missing)
## 
## Node number 1767: 13 observations
##   predicted class=B2  expected loss=0.4615385  P(node) =4.730662e-05
##     class counts:     1     7     2     2     1
##    probabilities: 0.077 0.538 0.154 0.154 0.077 
## 
## Node number 1772: 1057 observations,    complexity param=7.748763e-05
##   predicted class=B1  expected loss=0.589404  P(node) =0.003846392
##     class counts:   434   420   145    54     4
##    probabilities: 0.411 0.397 0.137 0.051 0.004 
##   left son=3544 (1008 obs) right son=3545 (49 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=1.0144070, (0 missing)
##       age               < 46.5   to the left,  improve=1.0088960, (0 missing)
##       reimbursement2008 < 2535   to the right, improve=0.9481247, (0 missing)
##       copd              < 0.5    to the left,  improve=0.6576908, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.5672579, (0 missing)
## 
## Node number 1773: 44 observations
##   predicted class=B2  expected loss=0.4545455  P(node) =0.0001601147
##     class counts:    11    24     5     3     1
##    probabilities: 0.250 0.545 0.114 0.068 0.023 
## 
## Node number 1774: 403 observations
##   predicted class=B2  expected loss=0.5756824  P(node) =0.001466505
##     class counts:   130   171    70    28     4
##    probabilities: 0.323 0.424 0.174 0.069 0.010 
## 
## Node number 1775: 29 observations
##   predicted class=B1  expected loss=0.5517241  P(node) =0.0001055301
##     class counts:    13     7     3     6     0
##    probabilities: 0.448 0.241 0.103 0.207 0.000 
## 
## Node number 1802: 684 observations,    complexity param=5.258089e-05
##   predicted class=B1  expected loss=0.5146199  P(node) =0.002489056
##     class counts:   332   231    95    26     0
##    probabilities: 0.485 0.338 0.139 0.038 0.000 
##   left son=3604 (286 obs) right son=3605 (398 obs)
##   Primary splits:
##       reimbursement2008 < 4365   to the left,  improve=1.4254390, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.1902870, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.8341619, (0 missing)
##       age               < 34     to the left,  improve=0.8175360, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.3590913, (0 missing)
## 
## Node number 1803: 9 observations
##   predicted class=B3  expected loss=0.4444444  P(node) =3.275073e-05
##     class counts:     3     1     5     0     0
##    probabilities: 0.333 0.111 0.556 0.000 0.000 
## 
## Node number 1810: 1608 observations
##   predicted class=B1  expected loss=0.5062189  P(node) =0.005851465
##     class counts:   794   529   193    82    10
##    probabilities: 0.494 0.329 0.120 0.051 0.006 
## 
## Node number 1811: 70 observations
##   predicted class=B2  expected loss=0.4857143  P(node) =0.0002547279
##     class counts:    26    36     6     2     0
##    probabilities: 0.371 0.514 0.086 0.029 0.000 
## 
## Node number 1812: 405 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5012346  P(node) =0.001473783
##     class counts:   202   140    42    18     3
##    probabilities: 0.499 0.346 0.104 0.044 0.007 
##   left son=3624 (329 obs) right son=3625 (76 obs)
##   Primary splits:
##       age               < 83.5   to the left,  improve=1.8474760, (0 missing)
##       reimbursement2008 < 14045  to the left,  improve=1.4437850, (0 missing)
##       kidney            < 0.5    to the right, improve=1.0197570, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.4573240, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.4260458, (0 missing)
## 
## Node number 1813: 452 observations,    complexity param=8.855729e-05
##   predicted class=B1  expected loss=0.579646  P(node) =0.001644815
##     class counts:   190   173    58    30     1
##    probabilities: 0.420 0.383 0.128 0.066 0.002 
##   left son=3626 (362 obs) right son=3627 (90 obs)
##   Primary splits:
##       reimbursement2008 < 3875   to the right, improve=2.645100, (0 missing)
##       age               < 84.5   to the left,  improve=2.429876, (0 missing)
##       bucket2008        < 2.5    to the right, improve=1.612268, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.063100, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.663279, (0 missing)
##   Surrogate splits:
##       age < 32     to the right, agree=0.803, adj=0.011, (0 split)
## 
## Node number 1820: 177 observations
##   predicted class=B1  expected loss=0.559322  P(node) =0.0006440978
##     class counts:    78    62    26    11     0
##    probabilities: 0.441 0.350 0.147 0.062 0.000 
## 
## Node number 1821: 519 observations
##   predicted class=B2  expected loss=0.6088632  P(node) =0.001888626
##     class counts:   154   203    86    64    12
##    probabilities: 0.297 0.391 0.166 0.123 0.023 
## 
## Node number 1826: 25 observations
##   predicted class=B1  expected loss=0.32  P(node) =9.097426e-05
##     class counts:    17     7     1     0     0
##    probabilities: 0.680 0.280 0.040 0.000 0.000 
## 
## Node number 1827: 357 observations,    complexity param=5.313437e-05
##   predicted class=B2  expected loss=0.5826331  P(node) =0.001299112
##     class counts:   140   149    48    19     1
##    probabilities: 0.392 0.417 0.134 0.053 0.003 
##   left son=3654 (91 obs) right son=3655 (266 obs)
##   Primary splits:
##       age               < 80.5   to the right, improve=2.1730570, (0 missing)
##       reimbursement2008 < 4405   to the right, improve=2.0106540, (0 missing)
##       depression        < 0.5    to the left,  improve=0.7793758, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.7738464, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.6298514, (0 missing)
## 
## Node number 1846: 39 observations
##   predicted class=B1  expected loss=0.4871795  P(node) =0.0001419198
##     class counts:    20    12     4     3     0
##    probabilities: 0.513 0.308 0.103 0.077 0.000 
## 
## Node number 1847: 254 observations
##   predicted class=B2  expected loss=0.5787402  P(node) =0.0009242985
##     class counts:    76   107    44    27     0
##    probabilities: 0.299 0.421 0.173 0.106 0.000 
## 
## Node number 1848: 132 observations,    complexity param=6.088314e-05
##   predicted class=B1  expected loss=0.5909091  P(node) =0.0004803441
##     class counts:    54    42    26     9     1
##    probabilities: 0.409 0.318 0.197 0.068 0.008 
##   left son=3696 (105 obs) right son=3697 (27 obs)
##   Primary splits:
##       age               < 84.5   to the left,  improve=4.2569990, (0 missing)
##       reimbursement2008 < 13440  to the left,  improve=1.5425260, (0 missing)
##       ihd               < 0.5    to the right, improve=1.3693110, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.4363743, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.4353832, (0 missing)
## 
## Node number 1849: 88 observations
##   predicted class=B3  expected loss=0.6477273  P(node) =0.0003202294
##     class counts:    23    24    31     7     3
##    probabilities: 0.261 0.273 0.352 0.080 0.034 
## 
## Node number 1898: 54 observations
##   predicted class=B3  expected loss=0.5555556  P(node) =0.0001965044
##     class counts:     8    14    24     7     1
##    probabilities: 0.148 0.259 0.444 0.130 0.019 
## 
## Node number 1899: 57 observations
##   predicted class=B2  expected loss=0.5614035  P(node) =0.0002074213
##     class counts:     5    25    15    10     2
##    probabilities: 0.088 0.439 0.263 0.175 0.035 
## 
## Node number 1924: 1127 observations
##   predicted class=B1  expected loss=0.4960071  P(node) =0.00410112
##     class counts:   568   377   131    47     4
##    probabilities: 0.504 0.335 0.116 0.042 0.004 
## 
## Node number 1925: 509 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5658153  P(node) =0.001852236
##     class counts:   221   185    67    33     3
##    probabilities: 0.434 0.363 0.132 0.065 0.006 
##   left son=3850 (137 obs) right son=3851 (372 obs)
##   Primary splits:
##       reimbursement2008 < 3775   to the left,  improve=1.6880360, (0 missing)
##       depression        < 0.5    to the left,  improve=1.6361880, (0 missing)
##       age               < 96.5   to the left,  improve=1.5026800, (0 missing)
##       copd              < 0.5    to the left,  improve=1.2566690, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.7418596, (0 missing)
## 
## Node number 1926: 339 observations,    complexity param=9.409212e-05
##   predicted class=B1  expected loss=0.5575221  P(node) =0.001233611
##     class counts:   150   127    45    16     1
##    probabilities: 0.442 0.375 0.133 0.047 0.003 
##   left son=3852 (211 obs) right son=3853 (128 obs)
##   Primary splits:
##       reimbursement2008 < 4905   to the left,  improve=1.6963240, (0 missing)
##       age               < 45     to the right, improve=1.4829560, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.2573130, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.6055009, (0 missing)
##       copd              < 0.5    to the left,  improve=0.2708942, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.687, adj=0.172, (0 split)
##       copd       < 0.5    to the left,  agree=0.664, adj=0.109, (0 split)
##       stroke     < 0.5    to the left,  agree=0.652, adj=0.078, (0 split)
## 
## Node number 1927: 239 observations,    complexity param=8.855729e-05
##   predicted class=B2  expected loss=0.5439331  P(node) =0.0008697139
##     class counts:    70   109    47    11     2
##    probabilities: 0.293 0.456 0.197 0.046 0.008 
##   left son=3854 (181 obs) right son=3855 (58 obs)
##   Primary splits:
##       copd              < 0.5    to the left,  improve=6.3468870, (0 missing)
##       reimbursement2008 < 5790   to the right, improve=1.7891020, (0 missing)
##       age               < 60.5   to the left,  improve=1.2691270, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.8740764, (0 missing)
##       stroke            < 0.5    to the right, improve=0.6684821, (0 missing)
##   Surrogate splits:
##       age < 35     to the right, agree=0.762, adj=0.017, (0 split)
## 
## Node number 1928: 798 observations
##   predicted class=B1  expected loss=0.5075188  P(node) =0.002903898
##     class counts:   393   256    95    51     3
##    probabilities: 0.492 0.321 0.119 0.064 0.004 
## 
## Node number 1929: 565 observations,    complexity param=6.088314e-05
##   predicted class=B1  expected loss=0.6247788  P(node) =0.002056018
##     class counts:   212   179   124    41     9
##    probabilities: 0.375 0.317 0.219 0.073 0.016 
##   left son=3858 (116 obs) right son=3859 (449 obs)
##   Primary splits:
##       stroke            < 0.5    to the right, improve=4.0381830, (0 missing)
##       reimbursement2008 < 31655  to the left,  improve=2.7523450, (0 missing)
##       age               < 42.5   to the right, improve=1.7655450, (0 missing)
##       bucket2008        < 4.5    to the left,  improve=1.4692280, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.5615109, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 61780  to the right, agree=0.8, adj=0.026, (0 split)
## 
## Node number 1930: 1953 observations,    complexity param=9.962695e-05
##   predicted class=B1  expected loss=0.578085  P(node) =0.007106909
##     class counts:   824   782   251    88     8
##    probabilities: 0.422 0.400 0.129 0.045 0.004 
##   left son=3860 (343 obs) right son=3861 (1610 obs)
##   Primary splits:
##       reimbursement2008 < 3415   to the left,  improve=3.4037160, (0 missing)
##       age               < 42.5   to the left,  improve=3.2783080, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.6509623, (0 missing)
##       copd              < 0.5    to the left,  improve=0.5598170, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.2946889, (0 missing)
## 
## Node number 1931: 2247 observations,    complexity param=0.0001605101
##   predicted class=B2  expected loss=0.5959057  P(node) =0.008176767
##     class counts:   795   908   383   148    13
##    probabilities: 0.354 0.404 0.170 0.066 0.006 
##   left son=3862 (866 obs) right son=3863 (1381 obs)
##   Primary splits:
##       reimbursement2008 < 5335   to the right, improve=3.344298, (0 missing)
##       copd              < 0.5    to the left,  improve=2.798571, (0 missing)
##       age               < 68.5   to the left,  improve=2.255236, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.653597, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.448247, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.678, adj=0.165, (0 split)
##       age        < 34.5   to the left,  agree=0.616, adj=0.005, (0 split)
## 
## Node number 1932: 1987 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5546049  P(node) =0.007230634
##     class counts:   662   885   320   111     9
##    probabilities: 0.333 0.445 0.161 0.056 0.005 
##   left son=3864 (1964 obs) right son=3865 (23 obs)
##   Primary splits:
##       age               < 98.5   to the left,  improve=3.328502, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.141909, (0 missing)
##       reimbursement2008 < 3085   to the left,  improve=3.126917, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=2.906536, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.332632, (0 missing)
## 
## Node number 1933: 941 observations
##   predicted class=B2  expected loss=0.5494155  P(node) =0.003424271
##     class counts:   242   424   186    79    10
##    probabilities: 0.257 0.451 0.198 0.084 0.011 
## 
## Node number 1934: 36 observations
##   predicted class=B1  expected loss=0.4444444  P(node) =0.0001310029
##     class counts:    20     8     8     0     0
##    probabilities: 0.556 0.222 0.222 0.000 0.000 
## 
## Node number 1935: 1428 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.6239496  P(node) =0.00519645
##     class counts:   455   537   282   146     8
##    probabilities: 0.319 0.376 0.197 0.102 0.006 
##   left son=3870 (837 obs) right son=3871 (591 obs)
##   Primary splits:
##       age               < 78.5   to the left,  improve=2.474561, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=2.118405, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.930317, (0 missing)
##       copd              < 0.5    to the left,  improve=1.447977, (0 missing)
##       reimbursement2008 < 8670   to the right, improve=1.324274, (0 missing)
## 
## Node number 1984: 101 observations
##   predicted class=B2  expected loss=0.5247525  P(node) =0.000367536
##     class counts:    33    48    15     4     1
##    probabilities: 0.327 0.475 0.149 0.040 0.010 
## 
## Node number 1985: 471 observations,    complexity param=5.811572e-05
##   predicted class=B1  expected loss=0.5732484  P(node) =0.001713955
##     class counts:   201   135    77    53     5
##    probabilities: 0.427 0.287 0.163 0.113 0.011 
##   left son=3970 (346 obs) right son=3971 (125 obs)
##   Primary splits:
##       osteoporosis      < 0.5    to the left,  improve=2.506365, (0 missing)
##       reimbursement2008 < 11515  to the left,  improve=2.004779, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.922393, (0 missing)
##       age               < 72.5   to the right, improve=1.840715, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=1.312872, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 3600   to the right, agree=0.737, adj=0.008, (0 split)
## 
## Node number 1986: 9 observations
##   predicted class=B1  expected loss=0.2222222  P(node) =3.275073e-05
##     class counts:     7     2     0     0     0
##    probabilities: 0.778 0.222 0.000 0.000 0.000 
## 
## Node number 1987: 383 observations
##   predicted class=B2  expected loss=0.5744125  P(node) =0.001393726
##     class counts:   115   163    75    25     5
##    probabilities: 0.300 0.426 0.196 0.065 0.013 
## 
## Node number 1990: 2424 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.6575908  P(node) =0.008820864
##     class counts:   663   830   547   335    49
##    probabilities: 0.274 0.342 0.226 0.138 0.020 
##   left son=3980 (1234 obs) right son=3981 (1190 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=3.612677, (0 missing)
##       age               < 67.5   to the right, improve=3.329297, (0 missing)
##       copd              < 0.5    to the left,  improve=3.109296, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=2.737555, (0 missing)
##       reimbursement2008 < 9205   to the right, improve=2.610291, (0 missing)
##   Surrogate splits:
##       alzheimers        < 0.5    to the left,  agree=0.552, adj=0.087, (0 split)
##       copd              < 0.5    to the left,  agree=0.540, adj=0.064, (0 split)
##       age               < 53.5   to the right, agree=0.526, adj=0.035, (0 split)
##       reimbursement2008 < 12525  to the left,  agree=0.522, adj=0.026, (0 split)
##       heart.failure     < 0.5    to the left,  agree=0.520, adj=0.023, (0 split)
## 
## Node number 1991: 1226 observations
##   predicted class=B2  expected loss=0.5831974  P(node) =0.004461378
##     class counts:   254   511   265   175    21
##    probabilities: 0.207 0.417 0.216 0.143 0.017 
## 
## Node number 2018: 293 observations
##   predicted class=B2  expected loss=0.4914676  P(node) =0.001066218
##     class counts:    20   149    81    39     4
##    probabilities: 0.068 0.509 0.276 0.133 0.014 
## 
## Node number 2019: 25 observations
##   predicted class=B3  expected loss=0.4  P(node) =9.097426e-05
##     class counts:     3     1    15     6     0
##    probabilities: 0.120 0.040 0.600 0.240 0.000 
## 
## Node number 2030: 237 observations,    complexity param=6.088314e-05
##   predicted class=B4  expected loss=0.6329114  P(node) =0.000862436
##     class counts:    13    76    49    87    12
##    probabilities: 0.055 0.321 0.207 0.367 0.051 
##   left son=4060 (62 obs) right son=4061 (175 obs)
##   Primary splits:
##       cancer            < 0.5    to the right, improve=2.618202, (0 missing)
##       reimbursement2008 < 90420  to the right, improve=2.488954, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=2.039633, (0 missing)
##       age               < 61.5   to the left,  improve=1.881916, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.753135, (0 missing)
## 
## Node number 2031: 14 observations
##   predicted class=B4  expected loss=0.2142857  P(node) =5.094559e-05
##     class counts:     0     1     1    11     1
##    probabilities: 0.000 0.071 0.071 0.786 0.071 
## 
## Node number 2032: 72 observations
##   predicted class=B1  expected loss=0.5694444  P(node) =0.0002620059
##     class counts:    31    18    11     8     4
##    probabilities: 0.431 0.250 0.153 0.111 0.056 
## 
## Node number 2033: 1245 observations
##   predicted class=B2  expected loss=0.6714859  P(node) =0.004530518
##     class counts:   238   409   223   305    70
##    probabilities: 0.191 0.329 0.179 0.245 0.056 
## 
## Node number 2034: 191 observations,    complexity param=7.748763e-05
##   predicted class=B2  expected loss=0.6753927  P(node) =0.0006950434
##     class counts:    29    62    44    42    14
##    probabilities: 0.152 0.325 0.230 0.220 0.073 
##   left son=4068 (172 obs) right son=4069 (19 obs)
##   Primary splits:
##       age               < 64.5   to the right, improve=2.5420870, (0 missing)
##       reimbursement2008 < 71460  to the left,  improve=2.3514440, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.2597430, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.9221356, (0 missing)
##       ihd               < 0.5    to the right, improve=0.7384918, (0 missing)
## 
## Node number 2035: 981 observations,    complexity param=9.962695e-05
##   predicted class=B4  expected loss=0.6788991  P(node) =0.00356983
##     class counts:   243   203   158   315    62
##    probabilities: 0.248 0.207 0.161 0.321 0.063 
##   left son=4070 (468 obs) right son=4071 (513 obs)
##   Primary splits:
##       reimbursement2008 < 23175  to the left,  improve=5.196818, (0 missing)
##       alzheimers        < 0.5    to the right, improve=3.174409, (0 missing)
##       ihd               < 0.5    to the left,  improve=2.640760, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.689264, (0 missing)
##       age               < 97.5   to the right, improve=1.688586, (0 missing)
##   Surrogate splits:
##       bucket2008    < 3.5    to the left,  agree=0.777, adj=0.532, (0 split)
##       heart.failure < 0.5    to the left,  agree=0.534, adj=0.024, (0 split)
##       age           < 53.5   to the left,  agree=0.531, adj=0.017, (0 split)
##       stroke        < 0.5    to the right, agree=0.525, adj=0.004, (0 split)
## 
## Node number 3304: 265 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.490566  P(node) =0.0009643272
##     class counts:   135    82    34    13     1
##    probabilities: 0.509 0.309 0.128 0.049 0.004 
##   left son=6608 (251 obs) right son=6609 (14 obs)
##   Primary splits:
##       stroke            < 0.5    to the left,  improve=3.807509, (0 missing)
##       ihd               < 0.5    to the left,  improve=2.996787, (0 missing)
##       cancer            < 0.5    to the left,  improve=2.863288, (0 missing)
##       reimbursement2008 < 1815   to the left,  improve=1.417998, (0 missing)
##       depression        < 0.5    to the left,  improve=1.227469, (0 missing)
## 
## Node number 3305: 290 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5931034  P(node) =0.001055301
##     class counts:   118   111    42    16     3
##    probabilities: 0.407 0.383 0.145 0.055 0.010 
##   left son=6610 (213 obs) right son=6611 (77 obs)
##   Primary splits:
##       age               < 81.5   to the left,  improve=1.9355560, (0 missing)
##       reimbursement2008 < 2015   to the right, improve=1.1719950, (0 missing)
##       ihd               < 0.5    to the right, improve=0.8443893, (0 missing)
##       stroke            < 0.5    to the right, improve=0.5640543, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.4757090, (0 missing)
## 
## Node number 3470: 840 observations,    complexity param=6.088314e-05
##   predicted class=B1  expected loss=0.5452381  P(node) =0.003056735
##     class counts:   382   309   103    41     5
##    probabilities: 0.455 0.368 0.123 0.049 0.006 
##   left son=6940 (71 obs) right son=6941 (769 obs)
##   Primary splits:
##       age               < 54.5   to the left,  improve=2.5793890, (0 missing)
##       kidney            < 0.5    to the left,  improve=2.0319410, (0 missing)
##       depression        < 0.5    to the left,  improve=1.3091120, (0 missing)
##       reimbursement2008 < 2945   to the right, improve=1.1530320, (0 missing)
##       copd              < 0.5    to the left,  improve=0.9762638, (0 missing)
## 
## Node number 3471: 217 observations
##   predicted class=B2  expected loss=0.5345622  P(node) =0.0007896566
##     class counts:    76   101    34     6     0
##    probabilities: 0.350 0.465 0.157 0.028 0.000 
## 
## Node number 3500: 1099 observations,    complexity param=9.962695e-05
##   predicted class=B1  expected loss=0.5814377  P(node) =0.003999229
##     class counts:   460   388   168    76     7
##    probabilities: 0.419 0.353 0.153 0.069 0.006 
##   left son=7000 (1074 obs) right son=7001 (25 obs)
##   Primary splits:
##       age               < 95.5   to the left,  improve=2.515661, (0 missing)
##       copd              < 0.5    to the left,  improve=2.359857, (0 missing)
##       cancer            < 0.5    to the left,  improve=1.641148, (0 missing)
##       reimbursement2008 < 2575   to the right, improve=1.347245, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.145174, (0 missing)
## 
## Node number 3501: 653 observations,    complexity param=0.0001129105
##   predicted class=B2  expected loss=0.6309342  P(node) =0.002376248
##     class counts:   226   241   126    55     5
##    probabilities: 0.346 0.369 0.193 0.084 0.008 
##   left son=7002 (303 obs) right son=7003 (350 obs)
##   Primary splits:
##       reimbursement2008 < 2655   to the left,  improve=2.636734, (0 missing)
##       cancer            < 0.5    to the left,  improve=1.461370, (0 missing)
##       age               < 55.5   to the right, improve=1.350106, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.189997, (0 missing)
##       bucket2008        < 1.5    to the left,  improve=1.091246, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the left,  agree=0.548, adj=0.026, (0 split)
##       copd       < 0.5    to the right, agree=0.542, adj=0.013, (0 split)
##       age        < 47.5   to the left,  agree=0.539, adj=0.007, (0 split)
## 
## Node number 3508: 382 observations,    complexity param=6.272808e-05
##   predicted class=B2  expected loss=0.6230366  P(node) =0.001390087
##     class counts:   142   144    74    18     4
##    probabilities: 0.372 0.377 0.194 0.047 0.010 
##   left son=7016 (229 obs) right son=7017 (153 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=0.9679873, (0 missing)
##       reimbursement2008 < 2275   to the left,  improve=0.8225283, (0 missing)
##       age               < 75.5   to the right, improve=0.7274055, (0 missing)
##       cancer            < 0.5    to the right, improve=0.6895810, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.4333447, (0 missing)
##   Surrogate splits:
##       alzheimers < 0.5    to the left,  agree=0.620, adj=0.052, (0 split)
##       age        < 50.5   to the right, agree=0.613, adj=0.033, (0 split)
## 
## Node number 3509: 21 observations
##   predicted class=B1  expected loss=0.5714286  P(node) =7.641838e-05
##     class counts:     9     3     5     2     2
##    probabilities: 0.429 0.143 0.238 0.095 0.095 
## 
## Node number 3532: 322 observations
##   predicted class=B1  expected loss=0.5465839  P(node) =0.001171748
##     class counts:   146   119    43    14     0
##    probabilities: 0.453 0.370 0.134 0.043 0.000 
## 
## Node number 3533: 14 observations
##   predicted class=B2  expected loss=0.3571429  P(node) =5.094559e-05
##     class counts:     4     9     1     0     0
##    probabilities: 0.286 0.643 0.071 0.000 0.000 
## 
## Node number 3544: 1008 observations,    complexity param=7.748763e-05
##   predicted class=B1  expected loss=0.5853175  P(node) =0.003668082
##     class counts:   418   400   133    53     4
##    probabilities: 0.415 0.397 0.132 0.053 0.004 
##   left son=7088 (275 obs) right son=7089 (733 obs)
##   Primary splits:
##       reimbursement2008 < 2535   to the right, improve=0.9732083, (0 missing)
##       age               < 39     to the left,  improve=0.9699606, (0 missing)
##       copd              < 0.5    to the left,  improve=0.8468269, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.4615681, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.4416739, (0 missing)
##   Surrogate splits:
##       age < 36.5   to the left,  agree=0.728, adj=0.004, (0 split)
## 
## Node number 3545: 49 observations
##   predicted class=B2  expected loss=0.5918367  P(node) =0.0001783096
##     class counts:    16    20    12     1     0
##    probabilities: 0.327 0.408 0.245 0.020 0.000 
## 
## Node number 3604: 286 observations
##   predicted class=B1  expected loss=0.4685315  P(node) =0.001040746
##     class counts:   152    94    35     5     0
##    probabilities: 0.531 0.329 0.122 0.017 0.000 
## 
## Node number 3605: 398 observations,    complexity param=5.258089e-05
##   predicted class=B1  expected loss=0.5477387  P(node) =0.00144831
##     class counts:   180   137    60    21     0
##    probabilities: 0.452 0.344 0.151 0.053 0.000 
##   left son=7210 (340 obs) right son=7211 (58 obs)
##   Primary splits:
##       reimbursement2008 < 4700   to the right, improve=5.7797840, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.1372610, (0 missing)
##       age               < 34.5   to the left,  improve=0.9964329, (0 missing)
##       bucket2008        < 2.5    to the right, improve=0.5848011, (0 missing)
##       kidney            < 0.5    to the right, improve=0.4151452, (0 missing)
## 
## Node number 3624: 329 observations
##   predicted class=B1  expected loss=0.4832827  P(node) =0.001197221
##     class counts:   170   105    35    16     3
##    probabilities: 0.517 0.319 0.106 0.049 0.009 
## 
## Node number 3625: 76 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.5394737  P(node) =0.0002765618
##     class counts:    32    35     7     2     0
##    probabilities: 0.421 0.461 0.092 0.026 0.000 
##   left son=7250 (21 obs) right son=7251 (55 obs)
##   Primary splits:
##       reimbursement2008 < 6785   to the right, improve=3.2066300, (0 missing)
##       bucket2008        < 2.5    to the right, improve=3.1159910, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.9967220, (0 missing)
##       age               < 85.5   to the right, improve=1.1176690, (0 missing)
##       kidney            < 0.5    to the right, improve=0.9258269, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.921, adj=0.714, (0 split)
##       kidney     < 0.5    to the right, agree=0.789, adj=0.238, (0 split)
## 
## Node number 3626: 362 observations
##   predicted class=B1  expected loss=0.5552486  P(node) =0.001317307
##     class counts:   161   128    47    25     1
##    probabilities: 0.445 0.354 0.130 0.069 0.003 
## 
## Node number 3627: 90 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.5  P(node) =0.0003275073
##     class counts:    29    45    11     5     0
##    probabilities: 0.322 0.500 0.122 0.056 0.000 
##   left son=7254 (21 obs) right son=7255 (69 obs)
##   Primary splits:
##       age               < 69.5   to the left,  improve=3.1544510, (0 missing)
##       alzheimers        < 0.5    to the right, improve=3.1535260, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.7000000, (0 missing)
##       reimbursement2008 < 3185   to the left,  improve=1.5133190, (0 missing)
##       copd              < 0.5    to the right, improve=0.3083333, (0 missing)
## 
## Node number 3654: 91 observations
##   predicted class=B1  expected loss=0.5054945  P(node) =0.0003311463
##     class counts:    45    31     9     6     0
##    probabilities: 0.495 0.341 0.099 0.066 0.000 
## 
## Node number 3655: 266 observations
##   predicted class=B2  expected loss=0.556391  P(node) =0.0009679661
##     class counts:    95   118    39    13     1
##    probabilities: 0.357 0.444 0.147 0.049 0.004 
## 
## Node number 3696: 105 observations
##   predicted class=B1  expected loss=0.5238095  P(node) =0.0003820919
##     class counts:    50    35    16     3     1
##    probabilities: 0.476 0.333 0.152 0.029 0.010 
## 
## Node number 3697: 27 observations
##   predicted class=B3  expected loss=0.6296296  P(node) =9.82522e-05
##     class counts:     4     7    10     6     0
##    probabilities: 0.148 0.259 0.370 0.222 0.000 
## 
## Node number 3850: 137 observations
##   predicted class=B1  expected loss=0.4963504  P(node) =0.000498539
##     class counts:    69    41    17     9     1
##    probabilities: 0.504 0.299 0.124 0.066 0.007 
## 
## Node number 3851: 372 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5913978  P(node) =0.001353697
##     class counts:   152   144    50    24     2
##    probabilities: 0.409 0.387 0.134 0.065 0.005 
##   left son=7702 (330 obs) right son=7703 (42 obs)
##   Primary splits:
##       reimbursement2008 < 4055   to the right, improve=3.5107950, (0 missing)
##       age               < 96     to the left,  improve=2.0766610, (0 missing)
##       depression        < 0.5    to the left,  improve=1.3295540, (0 missing)
##       copd              < 0.5    to the left,  improve=1.2612770, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.1950857, (0 missing)
## 
## Node number 3852: 211 observations,    complexity param=9.409212e-05
##   predicted class=B1  expected loss=0.563981  P(node) =0.0007678228
##     class counts:    92    89    23     7     0
##    probabilities: 0.436 0.422 0.109 0.033 0.000 
##   left son=7704 (142 obs) right son=7705 (69 obs)
##   Primary splits:
##       reimbursement2008 < 4075   to the left,  improve=3.4884480, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.9268848, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.8762896, (0 missing)
##       age               < 95     to the right, improve=0.6396384, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.3979516, (0 missing)
##   Surrogate splits:
##       age < 96.5   to the left,  agree=0.687, adj=0.043, (0 split)
## 
## Node number 3853: 128 observations
##   predicted class=B1  expected loss=0.546875  P(node) =0.0004657882
##     class counts:    58    38    22     9     1
##    probabilities: 0.453 0.297 0.172 0.070 0.008 
## 
## Node number 3854: 181 observations
##   predicted class=B2  expected loss=0.4861878  P(node) =0.0006586537
##     class counts:    56    93    23     7     2
##    probabilities: 0.309 0.514 0.127 0.039 0.011 
## 
## Node number 3855: 58 observations
##   predicted class=B3  expected loss=0.5862069  P(node) =0.0002110603
##     class counts:    14    16    24     4     0
##    probabilities: 0.241 0.276 0.414 0.069 0.000 
## 
## Node number 3858: 116 observations,    complexity param=6.088314e-05
##   predicted class=B2  expected loss=0.5517241  P(node) =0.0004221206
##     class counts:    41    52    14     7     2
##    probabilities: 0.353 0.448 0.121 0.060 0.017 
##   left son=7716 (63 obs) right son=7717 (53 obs)
##   Primary splits:
##       age               < 74.5   to the right, improve=2.8010500, (0 missing)
##       reimbursement2008 < 17265  to the left,  improve=2.4722090, (0 missing)
##       bucket2008        < 4.5    to the left,  improve=1.9776500, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.5892240, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.5845524, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 15590  to the right, agree=0.603, adj=0.132, (0 split)
##       heart.failure     < 0.5    to the right, agree=0.578, adj=0.075, (0 split)
##       alzheimers        < 0.5    to the right, agree=0.560, adj=0.038, (0 split)
##       osteoporosis      < 0.5    to the left,  agree=0.552, adj=0.019, (0 split)
##       bucket2008        < 3.5    to the right, agree=0.552, adj=0.019, (0 split)
## 
## Node number 3859: 449 observations
##   predicted class=B1  expected loss=0.6191537  P(node) =0.001633898
##     class counts:   171   127   110    34     7
##    probabilities: 0.381 0.283 0.245 0.076 0.016 
## 
## Node number 3860: 343 observations
##   predicted class=B1  expected loss=0.5014577  P(node) =0.001248167
##     class counts:   171   126    33    11     2
##    probabilities: 0.499 0.367 0.096 0.032 0.006 
## 
## Node number 3861: 1610 observations,    complexity param=9.962695e-05
##   predicted class=B2  expected loss=0.5925466  P(node) =0.005858742
##     class counts:   653   656   218    77     6
##    probabilities: 0.406 0.407 0.135 0.048 0.004 
##   left son=7722 (43 obs) right son=7723 (1567 obs)
##   Primary splits:
##       age               < 42.5   to the left,  improve=2.9787580, (0 missing)
##       reimbursement2008 < 3475   to the right, improve=1.6291410, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.4315089, (0 missing)
##       copd              < 0.5    to the left,  improve=0.2703192, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.2492479, (0 missing)
## 
## Node number 3862: 866 observations,    complexity param=0.0001605101
##   predicted class=B1  expected loss=0.6120092  P(node) =0.003151348
##     class counts:   336   322   139    64     5
##    probabilities: 0.388 0.372 0.161 0.074 0.006 
##   left son=7724 (129 obs) right son=7725 (737 obs)
##   Primary splits:
##       reimbursement2008 < 8115   to the right, improve=2.0175360, (0 missing)
##       age               < 89.5   to the left,  improve=1.8274460, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.6689370, (0 missing)
##       bucket2008        < 2.5    to the right, improve=1.3462440, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.5970317, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.984, adj=0.891, (0 split)
## 
## Node number 3863: 1381 observations,    complexity param=7.19528e-05
##   predicted class=B2  expected loss=0.5756698  P(node) =0.005025418
##     class counts:   459   586   244    84     8
##    probabilities: 0.332 0.424 0.177 0.061 0.006 
##   left son=7726 (997 obs) right son=7727 (384 obs)
##   Primary splits:
##       copd              < 0.5    to the left,  improve=3.5627620, (0 missing)
##       age               < 37.5   to the right, improve=2.2016010, (0 missing)
##       reimbursement2008 < 5195   to the left,  improve=1.9417980, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.9283600, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.3841814, (0 missing)
##   Surrogate splits:
##       age               < 34     to the right, agree=0.723, adj=0.005, (0 split)
##       reimbursement2008 < 5325   to the left,  agree=0.723, adj=0.005, (0 split)
## 
## Node number 3864: 1964 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.552444  P(node) =0.007146938
##     class counts:   656   879   309   111     9
##    probabilities: 0.334 0.448 0.157 0.057 0.005 
##   left son=7728 (22 obs) right son=7729 (1942 obs)
##   Primary splits:
##       reimbursement2008 < 3085   to the left,  improve=3.418849, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.308540, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=2.919418, (0 missing)
##       age               < 66.5   to the right, improve=1.961336, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.448295, (0 missing)
## 
## Node number 3865: 23 observations
##   predicted class=B3  expected loss=0.5217391  P(node) =8.369632e-05
##     class counts:     6     6    11     0     0
##    probabilities: 0.261 0.261 0.478 0.000 0.000 
## 
## Node number 3870: 837 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.6356033  P(node) =0.003045818
##     class counts:   292   305   155    82     3
##    probabilities: 0.349 0.364 0.185 0.098 0.004 
##   left son=7740 (639 obs) right son=7741 (198 obs)
##   Primary splits:
##       reimbursement2008 < 21320  to the left,  improve=1.8992410, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.6748470, (0 missing)
##       age               < 49.5   to the left,  improve=1.4981360, (0 missing)
##       bucket2008        < 3.5    to the left,  improve=1.4460210, (0 missing)
##       stroke            < 0.5    to the right, improve=0.7571134, (0 missing)
##   Surrogate splits:
##       bucket2008 < 3.5    to the left,  agree=0.955, adj=0.808, (0 split)
## 
## Node number 3871: 591 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.607445  P(node) =0.002150632
##     class counts:   163   232   127    64     5
##    probabilities: 0.276 0.393 0.215 0.108 0.008 
##   left son=7742 (122 obs) right son=7743 (469 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the left,  improve=3.4607800, (0 missing)
##       reimbursement2008 < 8775   to the left,  improve=1.9199300, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.2822570, (0 missing)
##       copd              < 0.5    to the left,  improve=1.1325150, (0 missing)
##       age               < 80.5   to the right, improve=0.6677683, (0 missing)
## 
## Node number 3970: 346 observations
##   predicted class=B1  expected loss=0.5375723  P(node) =0.001259084
##     class counts:   160    92    52    37     5
##    probabilities: 0.462 0.266 0.150 0.107 0.014 
## 
## Node number 3971: 125 observations,    complexity param=5.811572e-05
##   predicted class=B2  expected loss=0.656  P(node) =0.0004548713
##     class counts:    41    43    25    16     0
##    probabilities: 0.328 0.344 0.200 0.128 0.000 
##   left son=7942 (106 obs) right son=7943 (19 obs)
##   Primary splits:
##       age               < 62     to the right, improve=3.3415930, (0 missing)
##       reimbursement2008 < 11475  to the left,  improve=2.2730020, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.7920000, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.7319750, (0 missing)
##       stroke            < 0.5    to the right, improve=0.5402967, (0 missing)
## 
## Node number 3980: 1234 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.6612642  P(node) =0.00449049
##     class counts:   374   418   252   160    30
##    probabilities: 0.303 0.339 0.204 0.130 0.024 
##   left son=7960 (349 obs) right son=7961 (885 obs)
##   Primary splits:
##       reimbursement2008 < 12135  to the right, improve=3.745241, (0 missing)
##       age               < 67.5   to the left,  improve=3.421516, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.338981, (0 missing)
##       bucket2008        < 2.5    to the right, improve=1.254047, (0 missing)
##       copd              < 0.5    to the right, improve=1.093433, (0 missing)
## 
## Node number 3981: 1190 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.6537815  P(node) =0.004330375
##     class counts:   289   412   295   175    19
##    probabilities: 0.243 0.346 0.248 0.147 0.016 
##   left son=7962 (547 obs) right son=7963 (643 obs)
##   Primary splits:
##       copd              < 0.5    to the left,  improve=2.655589, (0 missing)
##       age               < 82.5   to the right, improve=1.853724, (0 missing)
##       stroke            < 0.5    to the right, improve=1.583996, (0 missing)
##       reimbursement2008 < 6355   to the right, improve=1.325969, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.085887, (0 missing)
##   Surrogate splits:
##       heart.failure     < 0.5    to the left,  agree=0.562, adj=0.048, (0 split)
##       reimbursement2008 < 7315   to the left,  agree=0.555, adj=0.031, (0 split)
##       alzheimers        < 0.5    to the left,  agree=0.543, adj=0.005, (0 split)
##       age               < 28.5   to the left,  agree=0.542, adj=0.004, (0 split)
## 
## Node number 4060: 62 observations
##   predicted class=B2  expected loss=0.5806452  P(node) =0.0002256162
##     class counts:     3    26    17    15     1
##    probabilities: 0.048 0.419 0.274 0.242 0.016 
## 
## Node number 4061: 175 observations
##   predicted class=B4  expected loss=0.5885714  P(node) =0.0006368198
##     class counts:    10    50    32    72    11
##    probabilities: 0.057 0.286 0.183 0.411 0.063 
## 
## Node number 4068: 172 observations
##   predicted class=B2  expected loss=0.6511628  P(node) =0.0006259029
##     class counts:    27    60    35    39    11
##    probabilities: 0.157 0.349 0.203 0.227 0.064 
## 
## Node number 4069: 19 observations
##   predicted class=B3  expected loss=0.5263158  P(node) =6.914044e-05
##     class counts:     2     2     9     3     3
##    probabilities: 0.105 0.105 0.474 0.158 0.158 
## 
## Node number 4070: 468 observations,    complexity param=7.379774e-05
##   predicted class=B1  expected loss=0.7200855  P(node) =0.001703038
##     class counts:   131   112    77   122    26
##    probabilities: 0.280 0.239 0.165 0.261 0.056 
##   left son=8140 (457 obs) right son=8141 (11 obs)
##   Primary splits:
##       age               < 93.5   to the left,  improve=1.955850, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=1.902288, (0 missing)
##       reimbursement2008 < 19700  to the right, improve=1.873153, (0 missing)
##       ihd               < 0.5    to the right, improve=1.868954, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.716285, (0 missing)
## 
## Node number 4071: 513 observations
##   predicted class=B4  expected loss=0.6237817  P(node) =0.001866792
##     class counts:   112    91    81   193    36
##    probabilities: 0.218 0.177 0.158 0.376 0.070 
## 
## Node number 6608: 251 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.4701195  P(node) =0.0009133816
##     class counts:   133    73    33    11     1
##    probabilities: 0.530 0.291 0.131 0.044 0.004 
##   left son=13216 (235 obs) right son=13217 (16 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=2.6866090, (0 missing)
##       ihd               < 0.5    to the left,  improve=2.4584220, (0 missing)
##       reimbursement2008 < 1815   to the left,  improve=1.2636760, (0 missing)
##       age               < 60.5   to the right, improve=1.0616730, (0 missing)
##       depression        < 0.5    to the left,  improve=0.7871996, (0 missing)
## 
## Node number 6609: 14 observations
##   predicted class=B2  expected loss=0.3571429  P(node) =5.094559e-05
##     class counts:     2     9     1     2     0
##    probabilities: 0.143 0.643 0.071 0.143 0.000 
## 
## Node number 6610: 213 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5633803  P(node) =0.0007751007
##     class counts:    93    74    30    15     1
##    probabilities: 0.437 0.347 0.141 0.070 0.005 
##   left son=13220 (201 obs) right son=13221 (12 obs)
##   Primary splits:
##       age               < 44.5   to the right, improve=1.7572700, (0 missing)
##       reimbursement2008 < 2005   to the right, improve=1.0657280, (0 missing)
##       cancer            < 0.5    to the right, improve=0.3892571, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.3640621, (0 missing)
##       depression        < 0.5    to the right, improve=0.3467310, (0 missing)
## 
## Node number 6611: 77 observations
##   predicted class=B2  expected loss=0.5194805  P(node) =0.0002802007
##     class counts:    25    37    12     1     2
##    probabilities: 0.325 0.481 0.156 0.013 0.026 
## 
## Node number 6940: 71 observations
##   predicted class=B1  expected loss=0.4366197  P(node) =0.0002583669
##     class counts:    40    16    11     3     1
##    probabilities: 0.563 0.225 0.155 0.042 0.014 
## 
## Node number 6941: 769 observations,    complexity param=6.088314e-05
##   predicted class=B1  expected loss=0.5552666  P(node) =0.002798368
##     class counts:   342   293    92    38     4
##    probabilities: 0.445 0.381 0.120 0.049 0.005 
##   left son=13882 (472 obs) right son=13883 (297 obs)
##   Primary splits:
##       age               < 70.5   to the right, improve=2.5248320, (0 missing)
##       depression        < 0.5    to the left,  improve=1.8557100, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.7236880, (0 missing)
##       reimbursement2008 < 2665   to the right, improve=1.1252400, (0 missing)
##       copd              < 0.5    to the left,  improve=0.9387137, (0 missing)
## 
## Node number 7000: 1074 observations,    complexity param=7.19528e-05
##   predicted class=B1  expected loss=0.5772812  P(node) =0.003908254
##     class counts:   454   373   166    74     7
##    probabilities: 0.423 0.347 0.155 0.069 0.007 
##   left son=14000 (808 obs) right son=14001 (266 obs)
##   Primary splits:
##       copd              < 0.5    to the left,  improve=2.7468870, (0 missing)
##       cancer            < 0.5    to the left,  improve=1.8315690, (0 missing)
##       age               < 78.5   to the left,  improve=1.6074350, (0 missing)
##       reimbursement2008 < 2575   to the right, improve=1.2651380, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.9951466, (0 missing)
## 
## Node number 7001: 25 observations
##   predicted class=B2  expected loss=0.4  P(node) =9.097426e-05
##     class counts:     6    15     2     2     0
##    probabilities: 0.240 0.600 0.080 0.080 0.000 
## 
## Node number 7002: 303 observations
##   predicted class=B1  expected loss=0.6039604  P(node) =0.001102608
##     class counts:   120    99    62    21     1
##    probabilities: 0.396 0.327 0.205 0.069 0.003 
## 
## Node number 7003: 350 observations
##   predicted class=B2  expected loss=0.5942857  P(node) =0.00127364
##     class counts:   106   142    64    34     4
##    probabilities: 0.303 0.406 0.183 0.097 0.011 
## 
## Node number 7016: 229 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5938865  P(node) =0.0008333242
##     class counts:    93    82    44     8     2
##    probabilities: 0.406 0.358 0.192 0.035 0.009 
##   left son=14032 (15 obs) right son=14033 (214 obs)
##   Primary splits:
##       cancer            < 0.5    to the right, improve=1.9222650, (0 missing)
##       reimbursement2008 < 2515   to the left,  improve=1.4164780, (0 missing)
##       age               < 94     to the left,  improve=1.2547820, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.6631197, (0 missing)
##       copd              < 0.5    to the right, improve=0.2469242, (0 missing)
## 
## Node number 7017: 153 observations,    complexity param=6.272808e-05
##   predicted class=B2  expected loss=0.5947712  P(node) =0.0005567625
##     class counts:    49    62    30    10     2
##    probabilities: 0.320 0.405 0.196 0.065 0.013 
##   left son=14034 (14 obs) right son=14035 (139 obs)
##   Primary splits:
##       reimbursement2008 < 2545   to the right, improve=2.7113570, (0 missing)
##       age               < 45     to the left,  improve=1.6972360, (0 missing)
##       cancer            < 0.5    to the left,  improve=0.6348039, (0 missing)
##       copd              < 0.5    to the right, improve=0.3887797, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.2839287, (0 missing)
## 
## Node number 7088: 275 observations,    complexity param=7.748763e-05
##   predicted class=B2  expected loss=0.56  P(node) =0.001000717
##     class counts:   108   121    34    11     1
##    probabilities: 0.393 0.440 0.124 0.040 0.004 
##   left son=14176 (44 obs) right son=14177 (231 obs)
##   Primary splits:
##       age               < 63.5   to the left,  improve=2.2068400, (0 missing)
##       reimbursement2008 < 2555   to the right, improve=1.7374730, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.5968660, (0 missing)
##       copd              < 0.5    to the left,  improve=0.9046397, (0 missing)
##       heart.failure     < 0.5    to the right, improve=0.5279104, (0 missing)
## 
## Node number 7089: 733 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5770805  P(node) =0.002667365
##     class counts:   310   279    99    42     3
##    probabilities: 0.423 0.381 0.135 0.057 0.004 
##   left son=14178 (10 obs) right son=14179 (723 obs)
##   Primary splits:
##       age               < 97.5   to the right, improve=1.1550490, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.1107930, (0 missing)
##       reimbursement2008 < 2495   to the right, improve=0.7495829, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.7242328, (0 missing)
##       depression        < 0.5    to the left,  improve=0.5684301, (0 missing)
## 
## Node number 7210: 340 observations
##   predicted class=B1  expected loss=0.5088235  P(node) =0.00123725
##     class counts:   167   107    48    18     0
##    probabilities: 0.491 0.315 0.141 0.053 0.000 
## 
## Node number 7211: 58 observations
##   predicted class=B2  expected loss=0.4827586  P(node) =0.0002110603
##     class counts:    13    30    12     3     0
##    probabilities: 0.224 0.517 0.207 0.052 0.000 
## 
## Node number 7250: 21 observations
##   predicted class=B1  expected loss=0.3333333  P(node) =7.641838e-05
##     class counts:    14     5     2     0     0
##    probabilities: 0.667 0.238 0.095 0.000 0.000 
## 
## Node number 7251: 55 observations
##   predicted class=B2  expected loss=0.4545455  P(node) =0.0002001434
##     class counts:    18    30     5     2     0
##    probabilities: 0.327 0.545 0.091 0.036 0.000 
## 
## Node number 7254: 21 observations
##   predicted class=B1  expected loss=0.4285714  P(node) =7.641838e-05
##     class counts:    12     6     1     2     0
##    probabilities: 0.571 0.286 0.048 0.095 0.000 
## 
## Node number 7255: 69 observations
##   predicted class=B2  expected loss=0.4347826  P(node) =0.000251089
##     class counts:    17    39    10     3     0
##    probabilities: 0.246 0.565 0.145 0.043 0.000 
## 
## Node number 7702: 330 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.569697  P(node) =0.00120086
##     class counts:   142   119    44    23     2
##    probabilities: 0.430 0.361 0.133 0.070 0.006 
##   left son=15404 (309 obs) right son=15405 (21 obs)
##   Primary splits:
##       reimbursement2008 < 4185   to the right, improve=2.2681710, (0 missing)
##       age               < 96     to the left,  improve=2.1333520, (0 missing)
##       depression        < 0.5    to the left,  improve=0.7533962, (0 missing)
##       copd              < 0.5    to the left,  improve=0.6700147, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.3769465, (0 missing)
## 
## Node number 7703: 42 observations
##   predicted class=B2  expected loss=0.4047619  P(node) =0.0001528368
##     class counts:    10    25     6     1     0
##    probabilities: 0.238 0.595 0.143 0.024 0.000 
## 
## Node number 7704: 142 observations
##   predicted class=B1  expected loss=0.5  P(node) =0.0005167338
##     class counts:    71    51    15     5     0
##    probabilities: 0.500 0.359 0.106 0.035 0.000 
## 
## Node number 7705: 69 observations
##   predicted class=B2  expected loss=0.4492754  P(node) =0.000251089
##     class counts:    21    38     8     2     0
##    probabilities: 0.304 0.551 0.116 0.029 0.000 
## 
## Node number 7716: 63 observations
##   predicted class=B1  expected loss=0.5714286  P(node) =0.0002292551
##     class counts:    27    21    10     4     1
##    probabilities: 0.429 0.333 0.159 0.063 0.016 
## 
## Node number 7717: 53 observations
##   predicted class=B2  expected loss=0.4150943  P(node) =0.0001928654
##     class counts:    14    31     4     3     1
##    probabilities: 0.264 0.585 0.075 0.057 0.019 
## 
## Node number 7722: 43 observations
##   predicted class=B1  expected loss=0.3953488  P(node) =0.0001564757
##     class counts:    26    11     3     3     0
##    probabilities: 0.605 0.256 0.070 0.070 0.000 
## 
## Node number 7723: 1567 observations,    complexity param=9.962695e-05
##   predicted class=B2  expected loss=0.5883854  P(node) =0.005702267
##     class counts:   627   645   215    74     6
##    probabilities: 0.400 0.412 0.137 0.047 0.004 
##   left son=15446 (1527 obs) right son=15447 (40 obs)
##   Primary splits:
##       age               < 50.5   to the right, improve=1.7032880, (0 missing)
##       reimbursement2008 < 3475   to the right, improve=1.5459000, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.4552758, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.2471234, (0 missing)
##       copd              < 0.5    to the left,  improve=0.2014160, (0 missing)
## 
## Node number 7724: 129 observations
##   predicted class=B2  expected loss=0.5271318  P(node) =0.0004694272
##     class counts:    46    61    16     6     0
##    probabilities: 0.357 0.473 0.124 0.047 0.000 
## 
## Node number 7725: 737 observations,    complexity param=9.962695e-05
##   predicted class=B1  expected loss=0.6065129  P(node) =0.002681921
##     class counts:   290   261   123    58     5
##    probabilities: 0.393 0.354 0.167 0.079 0.007 
##   left son=15450 (703 obs) right son=15451 (34 obs)
##   Primary splits:
##       age               < 94.5   to the left,  improve=1.9050170, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.5567680, (0 missing)
##       reimbursement2008 < 6575   to the right, improve=1.5078350, (0 missing)
##       copd              < 0.5    to the left,  improve=0.5423379, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.5213862, (0 missing)
## 
## Node number 7726: 997 observations,    complexity param=7.19528e-05
##   predicted class=B2  expected loss=0.5927783  P(node) =0.003628054
##     class counts:   357   406   171    57     6
##    probabilities: 0.358 0.407 0.172 0.057 0.006 
##   left son=15452 (297 obs) right son=15453 (700 obs)
##   Primary splits:
##       age               < 69.5   to the left,  improve=2.4458440, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=2.2624190, (0 missing)
##       reimbursement2008 < 4135   to the right, improve=1.8635870, (0 missing)
##       stroke            < 0.5    to the right, improve=0.3191114, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.3114115, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 5325   to the right, agree=0.703, adj=0.003, (0 split)
## 
## Node number 7727: 384 observations
##   predicted class=B2  expected loss=0.53125  P(node) =0.001397365
##     class counts:   102   180    73    27     2
##    probabilities: 0.266 0.469 0.190 0.070 0.005 
## 
## Node number 7728: 22 observations
##   predicted class=B1  expected loss=0.3636364  P(node) =8.005735e-05
##     class counts:    14     5     1     2     0
##    probabilities: 0.636 0.227 0.045 0.091 0.000 
## 
## Node number 7729: 1942 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5499485  P(node) =0.007066881
##     class counts:   642   874   308   109     9
##    probabilities: 0.331 0.450 0.159 0.056 0.005 
##   left son=15458 (889 obs) right son=15459 (1053 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the left,  improve=3.215424, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=2.693064, (0 missing)
##       reimbursement2008 < 8025   to the left,  improve=2.054172, (0 missing)
##       age               < 66.5   to the right, improve=1.953237, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.238773, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 3815   to the left,  agree=0.545, adj=0.007, (0 split)
##       age               < 31.5   to the left,  agree=0.544, adj=0.003, (0 split)
## 
## Node number 7740: 639 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.6353678  P(node) =0.002325302
##     class counts:   233   221   124    59     2
##    probabilities: 0.365 0.346 0.194 0.092 0.003 
##   left son=15480 (83 obs) right son=15481 (556 obs)
##   Primary splits:
##       age               < 49.5   to the left,  improve=1.7453130, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.0790130, (0 missing)
##       reimbursement2008 < 10445  to the right, improve=1.0644190, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.9731198, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.6917546, (0 missing)
## 
## Node number 7741: 198 observations
##   predicted class=B2  expected loss=0.5757576  P(node) =0.0007205162
##     class counts:    59    84    31    23     1
##    probabilities: 0.298 0.424 0.157 0.116 0.005 
## 
## Node number 7742: 122 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5983607  P(node) =0.0004439544
##     class counts:    49    39    22    12     0
##    probabilities: 0.402 0.320 0.180 0.098 0.000 
##   left son=15484 (40 obs) right son=15485 (82 obs)
##   Primary splits:
##       reimbursement2008 < 11560  to the left,  improve=2.7817470, (0 missing)
##       age               < 80.5   to the right, improve=1.9103730, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.4632510, (0 missing)
##       copd              < 0.5    to the left,  improve=1.0641520, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.9890302, (0 missing)
## 
## Node number 7743: 469 observations
##   predicted class=B2  expected loss=0.5884861  P(node) =0.001706677
##     class counts:   114   193   105    52     5
##    probabilities: 0.243 0.412 0.224 0.111 0.011 
## 
## Node number 7942: 106 observations,    complexity param=5.811572e-05
##   predicted class=B1  expected loss=0.6320755  P(node) =0.0003857309
##     class counts:    39    39    16    12     0
##    probabilities: 0.368 0.368 0.151 0.113 0.000 
##   left son=15884 (93 obs) right son=15885 (13 obs)
##   Primary splits:
##       age               < 67.5   to the right, improve=2.3273090, (0 missing)
##       reimbursement2008 < 11575  to the left,  improve=1.8244140, (0 missing)
##       stroke            < 0.5    to the right, improve=0.5034792, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.4237564, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.3937905, (0 missing)
## 
## Node number 7943: 19 observations
##   predicted class=B3  expected loss=0.5263158  P(node) =6.914044e-05
##     class counts:     2     4     9     4     0
##    probabilities: 0.105 0.211 0.474 0.211 0.000 
## 
## Node number 7960: 349 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.6475645  P(node) =0.001270001
##     class counts:   123   103    55    57    11
##    probabilities: 0.352 0.295 0.158 0.163 0.032 
##   left son=15920 (331 obs) right son=15921 (18 obs)
##   Primary splits:
##       age               < 54     to the right, improve=2.0196730, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.9958000, (0 missing)
##       reimbursement2008 < 15235  to the left,  improve=1.7314800, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.9381122, (0 missing)
##       copd              < 0.5    to the right, improve=0.4818854, (0 missing)
## 
## Node number 7961: 885 observations
##   predicted class=B2  expected loss=0.6440678  P(node) =0.003220489
##     class counts:   251   315   197   103    19
##    probabilities: 0.284 0.356 0.223 0.116 0.021 
## 
## Node number 7962: 547 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.6709324  P(node) =0.001990517
##     class counts:   154   180   136    65    12
##    probabilities: 0.282 0.329 0.249 0.119 0.022 
##   left son=15924 (310 obs) right son=15925 (237 obs)
##   Primary splits:
##       reimbursement2008 < 9205   to the right, improve=2.7787810, (0 missing)
##       age               < 68.5   to the right, improve=2.5123800, (0 missing)
##       bucket2008        < 2.5    to the right, improve=0.9624740, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.6055315, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.5192530, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.835, adj=0.620, (0 split)
##       age        < 44.5   to the right, agree=0.581, adj=0.034, (0 split)
## 
## Node number 7963: 643 observations
##   predicted class=B2  expected loss=0.6391913  P(node) =0.002339858
##     class counts:   135   232   159   110     7
##    probabilities: 0.210 0.361 0.247 0.171 0.011 
## 
## Node number 8140: 457 observations,    complexity param=7.379774e-05
##   predicted class=B1  expected loss=0.7133479  P(node) =0.00166301
##     class counts:   131   107    73   120    26
##    probabilities: 0.287 0.234 0.160 0.263 0.057 
##   left son=16280 (398 obs) right son=16281 (59 obs)
##   Primary splits:
##       age               < 86.5   to the left,  improve=2.218362, (0 missing)
##       ihd               < 0.5    to the right, improve=2.044330, (0 missing)
##       reimbursement2008 < 19430  to the right, improve=1.853412, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.735004, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=1.352826, (0 missing)
## 
## Node number 8141: 11 observations
##   predicted class=B2  expected loss=0.5454545  P(node) =4.002868e-05
##     class counts:     0     5     4     2     0
##    probabilities: 0.000 0.455 0.364 0.182 0.000 
## 
## Node number 13216: 235 observations
##   predicted class=B1  expected loss=0.4510638  P(node) =0.0008551581
##     class counts:   129    64    30    11     1
##    probabilities: 0.549 0.272 0.128 0.047 0.004 
## 
## Node number 13217: 16 observations
##   predicted class=B2  expected loss=0.4375  P(node) =5.822353e-05
##     class counts:     4     9     3     0     0
##    probabilities: 0.250 0.562 0.188 0.000 0.000 
## 
## Node number 13220: 201 observations
##   predicted class=B1  expected loss=0.5472637  P(node) =0.0007314331
##     class counts:    91    67    29    14     0
##    probabilities: 0.453 0.333 0.144 0.070 0.000 
## 
## Node number 13221: 12 observations
##   predicted class=B2  expected loss=0.4166667  P(node) =4.366765e-05
##     class counts:     2     7     1     1     1
##    probabilities: 0.167 0.583 0.083 0.083 0.083 
## 
## Node number 13882: 472 observations,    complexity param=5.165842e-05
##   predicted class=B1  expected loss=0.5275424  P(node) =0.001717594
##     class counts:   223   163    57    25     4
##    probabilities: 0.472 0.345 0.121 0.053 0.008 
##   left son=27764 (343 obs) right son=27765 (129 obs)
##   Primary splits:
##       age               < 73.5   to the right, improve=4.630612, (0 missing)
##       reimbursement2008 < 2805   to the right, improve=1.597068, (0 missing)
##       depression        < 0.5    to the left,  improve=1.459900, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.335760, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.130037, (0 missing)
## 
## Node number 13883: 297 observations,    complexity param=6.088314e-05
##   predicted class=B2  expected loss=0.5622896  P(node) =0.001080774
##     class counts:   119   130    35    13     0
##    probabilities: 0.401 0.438 0.118 0.044 0.000 
##   left son=27766 (218 obs) right son=27767 (79 obs)
##   Primary splits:
##       osteoporosis      < 0.5    to the left,  improve=1.3951400, (0 missing)
##       reimbursement2008 < 2945   to the right, improve=1.0350230, (0 missing)
##       depression        < 0.5    to the left,  improve=0.9259259, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.7583938, (0 missing)
##       copd              < 0.5    to the left,  improve=0.3569379, (0 missing)
## 
## Node number 14000: 808 observations
##   predicted class=B1  expected loss=0.5569307  P(node) =0.002940288
##     class counts:   358   273   111    61     5
##    probabilities: 0.443 0.338 0.137 0.075 0.006 
## 
## Node number 14001: 266 observations,    complexity param=7.19528e-05
##   predicted class=B2  expected loss=0.6240602  P(node) =0.0009679661
##     class counts:    96   100    55    13     2
##    probabilities: 0.361 0.376 0.207 0.049 0.008 
##   left son=28002 (192 obs) right son=28003 (74 obs)
##   Primary splits:
##       reimbursement2008 < 2540   to the right, improve=2.9691060, (0 missing)
##       age               < 78.5   to the left,  improve=2.6852920, (0 missing)
##       cancer            < 0.5    to the right, improve=2.3754980, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.7018574, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.6157537, (0 missing)
##   Surrogate splits:
##       age < 50.5   to the right, agree=0.737, adj=0.054, (0 split)
## 
## Node number 14032: 15 observations
##   predicted class=B1  expected loss=0.3333333  P(node) =5.458456e-05
##     class counts:    10     2     3     0     0
##    probabilities: 0.667 0.133 0.200 0.000 0.000 
## 
## Node number 14033: 214 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.6121495  P(node) =0.0007787397
##     class counts:    83    80    41     8     2
##    probabilities: 0.388 0.374 0.192 0.037 0.009 
##   left son=28066 (169 obs) right son=28067 (45 obs)
##   Primary splits:
##       reimbursement2008 < 2515   to the left,  improve=1.6030020, (0 missing)
##       age               < 52.5   to the right, improve=0.9765448, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.7668533, (0 missing)
##       copd              < 0.5    to the right, improve=0.3681910, (0 missing)
##       ihd               < 0.5    to the right, improve=0.1207875, (0 missing)
## 
## Node number 14034: 14 observations
##   predicted class=B1  expected loss=0.3571429  P(node) =5.094559e-05
##     class counts:     9     2     2     1     0
##    probabilities: 0.643 0.143 0.143 0.071 0.000 
## 
## Node number 14035: 139 observations
##   predicted class=B2  expected loss=0.5683453  P(node) =0.0005058169
##     class counts:    40    60    28     9     2
##    probabilities: 0.288 0.432 0.201 0.065 0.014 
## 
## Node number 14176: 44 observations
##   predicted class=B2  expected loss=0.3863636  P(node) =0.0001601147
##     class counts:    14    27     2     1     0
##    probabilities: 0.318 0.614 0.045 0.023 0.000 
## 
## Node number 14177: 231 observations,    complexity param=7.748763e-05
##   predicted class=B1  expected loss=0.5930736  P(node) =0.0008406022
##     class counts:    94    94    32    10     1
##    probabilities: 0.407 0.407 0.139 0.043 0.004 
##   left son=28354 (169 obs) right son=28355 (62 obs)
##   Primary splits:
##       alzheimers        < 0.5    to the left,  improve=2.4583990, (0 missing)
##       reimbursement2008 < 2555   to the right, improve=1.0376560, (0 missing)
##       age               < 84.5   to the left,  improve=1.0243680, (0 missing)
##       copd              < 0.5    to the left,  improve=0.7240171, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.4410819, (0 missing)
##   Surrogate splits:
##       age < 87.5   to the left,  agree=0.745, adj=0.048, (0 split)
## 
## Node number 14178: 10 observations
##   predicted class=B1  expected loss=0.3  P(node) =3.63897e-05
##     class counts:     7     2     1     0     0
##    probabilities: 0.700 0.200 0.100 0.000 0.000 
## 
## Node number 14179: 723 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5809129  P(node) =0.002630976
##     class counts:   303   277    98    42     3
##    probabilities: 0.419 0.383 0.136 0.058 0.004 
##   left son=28358 (689 obs) right son=28359 (34 obs)
##   Primary splits:
##       age               < 90.5   to the left,  improve=1.6650270, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.5078050, (0 missing)
##       reimbursement2008 < 2495   to the right, improve=0.8133392, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.6699213, (0 missing)
##       depression        < 0.5    to the left,  improve=0.5296598, (0 missing)
## 
## Node number 15404: 309 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5728155  P(node) =0.001124442
##     class counts:   132   117    38    20     2
##    probabilities: 0.427 0.379 0.123 0.065 0.006 
##   left son=30808 (253 obs) right son=30809 (56 obs)
##   Primary splits:
##       reimbursement2008 < 4635   to the right, improve=2.0908250, (0 missing)
##       age               < 73.5   to the right, improve=1.8355900, (0 missing)
##       depression        < 0.5    to the left,  improve=0.6554201, (0 missing)
##       copd              < 0.5    to the left,  improve=0.3380891, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.2757170, (0 missing)
## 
## Node number 15405: 21 observations
##   predicted class=B1  expected loss=0.5238095  P(node) =7.641838e-05
##     class counts:    10     2     6     3     0
##    probabilities: 0.476 0.095 0.286 0.143 0.000 
## 
## Node number 15446: 1527 observations,    complexity param=9.962695e-05
##   predicted class=B2  expected loss=0.5854617  P(node) =0.005556708
##     class counts:   613   633   203    72     6
##    probabilities: 0.401 0.415 0.133 0.047 0.004 
##   left son=30892 (1478 obs) right son=30893 (49 obs)
##   Primary splits:
##       reimbursement2008 < 3465   to the right, improve=1.7561930, (0 missing)
##       age               < 59.5   to the right, improve=1.2446620, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.3864080, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.3262151, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.1237742, (0 missing)
## 
## Node number 15447: 40 observations
##   predicted class=B1  expected loss=0.65  P(node) =0.0001455588
##     class counts:    14    12    12     2     0
##    probabilities: 0.350 0.300 0.300 0.050 0.000 
## 
## Node number 15450: 703 observations,    complexity param=8.855729e-05
##   predicted class=B1  expected loss=0.598862  P(node) =0.002558196
##     class counts:   282   244   119    53     5
##    probabilities: 0.401 0.347 0.169 0.075 0.007 
##   left son=30900 (298 obs) right son=30901 (405 obs)
##   Primary splits:
##       reimbursement2008 < 6635   to the right, improve=1.9072210, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.4867600, (0 missing)
##       age               < 74.5   to the left,  improve=1.1374550, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.4608058, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.4586126, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.596, adj=0.047, (0 split)
##       age        < 35.5   to the left,  agree=0.578, adj=0.003, (0 split)
## 
## Node number 15451: 34 observations
##   predicted class=B2  expected loss=0.5  P(node) =0.000123725
##     class counts:     8    17     4     5     0
##    probabilities: 0.235 0.500 0.118 0.147 0.000 
## 
## Node number 15452: 297 observations,    complexity param=7.19528e-05
##   predicted class=B1  expected loss=0.5757576  P(node) =0.001080774
##     class counts:   126   113    45    12     1
##    probabilities: 0.424 0.380 0.152 0.040 0.003 
##   left son=30904 (274 obs) right son=30905 (23 obs)
##   Primary splits:
##       reimbursement2008 < 5065   to the left,  improve=2.3768610, (0 missing)
##       alzheimers        < 0.5    to the right, improve=2.2936150, (0 missing)
##       age               < 37.5   to the left,  improve=1.9456180, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=0.7719678, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.6098027, (0 missing)
## 
## Node number 15453: 700 observations
##   predicted class=B2  expected loss=0.5814286  P(node) =0.002547279
##     class counts:   231   293   126    45     5
##    probabilities: 0.330 0.419 0.180 0.064 0.007 
## 
## Node number 15458: 889 observations
##   predicted class=B2  expected loss=0.5714286  P(node) =0.003235045
##     class counts:   327   381   133    45     3
##    probabilities: 0.368 0.429 0.150 0.051 0.003 
## 
## Node number 15459: 1053 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5318139  P(node) =0.003831836
##     class counts:   315   493   175    64     6
##    probabilities: 0.299 0.468 0.166 0.061 0.006 
##   left son=30918 (721 obs) right son=30919 (332 obs)
##   Primary splits:
##       osteoporosis      < 0.5    to the left,  improve=2.319421, (0 missing)
##       age               < 65.5   to the right, improve=2.157808, (0 missing)
##       reimbursement2008 < 4195   to the left,  improve=2.005955, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.694776, (0 missing)
##       bucket2008        < 2.5    to the right, improve=0.685891, (0 missing)
## 
## Node number 15480: 83 observations
##   predicted class=B2  expected loss=0.5662651  P(node) =0.0003020345
##     class counts:    29    36     8    10     0
##    probabilities: 0.349 0.434 0.096 0.120 0.000 
## 
## Node number 15481: 556 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.6330935  P(node) =0.002023268
##     class counts:   204   185   116    49     2
##    probabilities: 0.367 0.333 0.209 0.088 0.004 
##   left son=30962 (368 obs) right son=30963 (188 obs)
##   Primary splits:
##       age               < 67.5   to the right, improve=1.7538220, (0 missing)
##       reimbursement2008 < 17290  to the right, improve=1.5233210, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.8892958, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.8663588, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=0.8033839, (0 missing)
## 
## Node number 15484: 40 observations
##   predicted class=B1  expected loss=0.425  P(node) =0.0001455588
##     class counts:    23     8     7     2     0
##    probabilities: 0.575 0.200 0.175 0.050 0.000 
## 
## Node number 15485: 82 observations
##   predicted class=B2  expected loss=0.6219512  P(node) =0.0002983956
##     class counts:    26    31    15    10     0
##    probabilities: 0.317 0.378 0.183 0.122 0.000 
## 
## Node number 15884: 93 observations,    complexity param=5.811572e-05
##   predicted class=B2  expected loss=0.5913978  P(node) =0.0003384243
##     class counts:    32    38    15     8     0
##    probabilities: 0.344 0.409 0.161 0.086 0.000 
##   left son=31768 (44 obs) right son=31769 (49 obs)
##   Primary splits:
##       reimbursement2008 < 6110   to the right, improve=2.8038180, (0 missing)
##       age               < 68.5   to the right, improve=0.9063337, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.4118188, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.3578690, (0 missing)
##       stroke            < 0.5    to the right, improve=0.3151562, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.785, adj=0.545, (0 split)
##       age        < 73.5   to the left,  agree=0.602, adj=0.159, (0 split)
##       alzheimers < 0.5    to the right, agree=0.538, adj=0.023, (0 split)
## 
## Node number 15885: 13 observations
##   predicted class=B1  expected loss=0.4615385  P(node) =4.730662e-05
##     class counts:     7     1     1     4     0
##    probabilities: 0.538 0.077 0.077 0.308 0.000 
## 
## Node number 15920: 331 observations
##   predicted class=B1  expected loss=0.6344411  P(node) =0.001204499
##     class counts:   121    94    53    53    10
##    probabilities: 0.366 0.284 0.160 0.160 0.030 
## 
## Node number 15921: 18 observations
##   predicted class=B2  expected loss=0.5  P(node) =6.550147e-05
##     class counts:     2     9     2     4     1
##    probabilities: 0.111 0.500 0.111 0.222 0.056 
## 
## Node number 15924: 310 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.6741935  P(node) =0.001128081
##     class counts:   101    99    64    37     9
##    probabilities: 0.326 0.319 0.206 0.119 0.029 
##   left son=31848 (50 obs) right son=31849 (260 obs)
##   Primary splits:
##       reimbursement2008 < 9955   to the left,  improve=3.5194040, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.4052180, (0 missing)
##       age               < 60.5   to the right, improve=1.3545900, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.9417634, (0 missing)
##       stroke            < 0.5    to the right, improve=0.4401818, (0 missing)
## 
## Node number 15925: 237 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.6582278  P(node) =0.000862436
##     class counts:    53    81    72    28     3
##    probabilities: 0.224 0.342 0.304 0.118 0.013 
##   left son=31850 (56 obs) right son=31851 (181 obs)
##   Primary splits:
##       age               < 67.5   to the left,  improve=3.14488400, (0 missing)
##       reimbursement2008 < 7130   to the left,  improve=2.11196700, (0 missing)
##       bucket2008        < 2.5    to the right, improve=0.86604090, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.39390990, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.05008339, (0 missing)
## 
## Node number 16280: 398 observations,    complexity param=7.379774e-05
##   predicted class=B1  expected loss=0.7236181  P(node) =0.00144831
##     class counts:   110   101    63    99    25
##    probabilities: 0.276 0.254 0.158 0.249 0.063 
##   left son=32560 (179 obs) right son=32561 (219 obs)
##   Primary splits:
##       alzheimers        < 0.5    to the right, improve=2.797541, (0 missing)
##       ihd               < 0.5    to the right, improve=2.182276, (0 missing)
##       reimbursement2008 < 15500  to the right, improve=1.710577, (0 missing)
##       stroke            < 0.5    to the right, improve=1.223226, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=1.211249, (0 missing)
##   Surrogate splits:
##       stroke            < 0.5    to the right, agree=0.563, adj=0.028, (0 split)
##       reimbursement2008 < 15625  to the left,  agree=0.563, adj=0.028, (0 split)
##       age               < 62.5   to the left,  agree=0.555, adj=0.011, (0 split)
##       osteoporosis      < 0.5    to the right, agree=0.553, adj=0.006, (0 split)
## 
## Node number 16281: 59 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.6440678  P(node) =0.0002146993
##     class counts:    21     6    10    21     1
##    probabilities: 0.356 0.102 0.169 0.356 0.017 
##   left son=32562 (18 obs) right son=32563 (41 obs)
##   Primary splits:
##       reimbursement2008 < 19680  to the right, improve=1.9754260, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.9501021, (0 missing)
##       bucket2008        < 3.5    to the right, improve=0.8931654, (0 missing)
##       age               < 90.5   to the right, improve=0.7250257, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.5260164, (0 missing)
##   Surrogate splits:
##       bucket2008 < 3.5    to the right, agree=0.847, adj=0.5, (0 split)
## 
## Node number 27764: 343 observations,    complexity param=5.165842e-05
##   predicted class=B1  expected loss=0.5568513  P(node) =0.001248167
##     class counts:   152   136    37    16     2
##    probabilities: 0.443 0.397 0.108 0.047 0.006 
##   left son=55528 (117 obs) right son=55529 (226 obs)
##   Primary splits:
##       reimbursement2008 < 2835   to the right, improve=1.9282960, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.1581140, (0 missing)
##       age               < 82.5   to the right, improve=1.0933820, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.0145490, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.9380155, (0 missing)
## 
## Node number 27765: 129 observations
##   predicted class=B1  expected loss=0.4496124  P(node) =0.0004694272
##     class counts:    71    27    20     9     2
##    probabilities: 0.550 0.209 0.155 0.070 0.016 
## 
## Node number 27766: 218 observations,    complexity param=6.088314e-05
##   predicted class=B1  expected loss=0.5733945  P(node) =0.0007932956
##     class counts:    93    89    28     8     0
##    probabilities: 0.427 0.408 0.128 0.037 0.000 
##   left son=55532 (194 obs) right son=55533 (24 obs)
##   Primary splits:
##       reimbursement2008 < 2945   to the left,  improve=1.9617420, (0 missing)
##       depression        < 0.5    to the left,  improve=0.6526821, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.4610298, (0 missing)
##       age               < 57.5   to the left,  improve=0.4574831, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.3559027, (0 missing)
## 
## Node number 27767: 79 observations
##   predicted class=B2  expected loss=0.4810127  P(node) =0.0002874787
##     class counts:    26    41     7     5     0
##    probabilities: 0.329 0.519 0.089 0.063 0.000 
## 
## Node number 28002: 192 observations,    complexity param=7.19528e-05
##   predicted class=B2  expected loss=0.59375  P(node) =0.0006986823
##     class counts:    72    78    29    11     2
##    probabilities: 0.375 0.406 0.151 0.057 0.010 
##   left son=56004 (124 obs) right son=56005 (68 obs)
##   Primary splits:
##       age               < 78.5   to the left,  improve=3.1968100, (0 missing)
##       reimbursement2008 < 2885   to the left,  improve=2.1236740, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.0053880, (0 missing)
##       bucket2008        < 1.5    to the left,  improve=0.7479369, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.5316513, (0 missing)
## 
## Node number 28003: 74 observations,    complexity param=7.19528e-05
##   predicted class=B3  expected loss=0.6486486  P(node) =0.0002692838
##     class counts:    24    22    26     2     0
##    probabilities: 0.324 0.297 0.351 0.027 0.000 
##   left son=56006 (8 obs) right son=56007 (66 obs)
##   Primary splits:
##       cancer            < 0.5    to the right, improve=6.4864860, (0 missing)
##       age               < 65     to the left,  improve=1.7666590, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.7622440, (0 missing)
##       reimbursement2008 < 2355   to the right, improve=1.0927360, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.8745462, (0 missing)
## 
## Node number 28066: 169 observations
##   predicted class=B1  expected loss=0.5798817  P(node) =0.000614986
##     class counts:    71    58    32     6     2
##    probabilities: 0.420 0.343 0.189 0.036 0.012 
## 
## Node number 28067: 45 observations
##   predicted class=B2  expected loss=0.5111111  P(node) =0.0001637537
##     class counts:    12    22     9     2     0
##    probabilities: 0.267 0.489 0.200 0.044 0.000 
## 
## Node number 28354: 169 observations
##   predicted class=B1  expected loss=0.5621302  P(node) =0.000614986
##     class counts:    74    60    26     8     1
##    probabilities: 0.438 0.355 0.154 0.047 0.006 
## 
## Node number 28355: 62 observations
##   predicted class=B2  expected loss=0.4516129  P(node) =0.0002256162
##     class counts:    20    34     6     2     0
##    probabilities: 0.323 0.548 0.097 0.032 0.000 
## 
## Node number 28358: 689 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5718433  P(node) =0.002507251
##     class counts:   295   261    92    38     3
##    probabilities: 0.428 0.379 0.134 0.055 0.004 
##   left son=56716 (367 obs) right son=56717 (322 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the left,  improve=1.5366830, (0 missing)
##       reimbursement2008 < 2185   to the right, improve=0.9498001, (0 missing)
##       age               < 67.5   to the left,  improve=0.9450906, (0 missing)
##       copd              < 0.5    to the left,  improve=0.5052370, (0 missing)
##       depression        < 0.5    to the left,  improve=0.4336301, (0 missing)
##   Surrogate splits:
##       copd              < 0.5    to the left,  agree=0.605, adj=0.155, (0 split)
##       age               < 85.5   to the left,  agree=0.543, adj=0.022, (0 split)
##       reimbursement2008 < 2515   to the left,  agree=0.541, adj=0.019, (0 split)
##       alzheimers        < 0.5    to the left,  agree=0.538, adj=0.012, (0 split)
## 
## Node number 28359: 34 observations
##   predicted class=B2  expected loss=0.5294118  P(node) =0.000123725
##     class counts:     8    16     6     4     0
##    probabilities: 0.235 0.471 0.176 0.118 0.000 
## 
## Node number 30808: 253 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5454545  P(node) =0.0009206595
##     class counts:   115    89    30    17     2
##    probabilities: 0.455 0.352 0.119 0.067 0.008 
##   left son=61616 (245 obs) right son=61617 (8 obs)
##   Primary splits:
##       age               < 96     to the left,  improve=1.6668230, (0 missing)
##       reimbursement2008 < 8170   to the left,  improve=1.5801570, (0 missing)
##       depression        < 0.5    to the left,  improve=0.8012407, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.6406559, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.4810539, (0 missing)
## 
## Node number 30809: 56 observations
##   predicted class=B2  expected loss=0.5  P(node) =0.0002037823
##     class counts:    17    28     8     3     0
##    probabilities: 0.304 0.500 0.143 0.054 0.000 
## 
## Node number 30892: 1478 observations,    complexity param=9.962695e-05
##   predicted class=B2  expected loss=0.5886333  P(node) =0.005378398
##     class counts:   600   608   199    67     4
##    probabilities: 0.406 0.411 0.135 0.045 0.003 
##   left son=61784 (759 obs) right son=61785 (719 obs)
##   Primary splits:
##       reimbursement2008 < 4655   to the left,  improve=1.4912330, (0 missing)
##       age               < 59.5   to the right, improve=1.4379920, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.4252592, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.4189515, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.1287486, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.566, adj=0.108, (0 split)
##       alzheimers < 0.5    to the left,  agree=0.535, adj=0.043, (0 split)
##       stroke     < 0.5    to the left,  agree=0.533, adj=0.040, (0 split)
##       copd       < 0.5    to the left,  agree=0.530, adj=0.033, (0 split)
##       age        < 82.5   to the left,  agree=0.526, adj=0.025, (0 split)
## 
## Node number 30893: 49 observations
##   predicted class=B2  expected loss=0.4897959  P(node) =0.0001783096
##     class counts:    13    25     4     5     2
##    probabilities: 0.265 0.510 0.082 0.102 0.041 
## 
## Node number 30900: 298 observations
##   predicted class=B1  expected loss=0.5503356  P(node) =0.001084413
##     class counts:   134    94    46    20     4
##    probabilities: 0.450 0.315 0.154 0.067 0.013 
## 
## Node number 30901: 405 observations,    complexity param=8.855729e-05
##   predicted class=B2  expected loss=0.6296296  P(node) =0.001473783
##     class counts:   148   150    73    33     1
##    probabilities: 0.365 0.370 0.180 0.081 0.002 
##   left son=61802 (137 obs) right son=61803 (268 obs)
##   Primary splits:
##       reimbursement2008 < 5685   to the left,  improve=1.4352860, (0 missing)
##       age               < 43     to the right, improve=1.2563810, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=0.8108852, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.3644866, (0 missing)
##       copd              < 0.5    to the left,  improve=0.3421456, (0 missing)
## 
## Node number 30904: 274 observations,    complexity param=7.19528e-05
##   predicted class=B1  expected loss=0.5583942  P(node) =0.0009970779
##     class counts:   121    99    42    11     1
##    probabilities: 0.442 0.361 0.153 0.040 0.004 
##   left son=61808 (174 obs) right son=61809 (100 obs)
##   Primary splits:
##       alzheimers        < 0.5    to the left,  improve=2.2349370, (0 missing)
##       age               < 37.5   to the left,  improve=1.7714310, (0 missing)
##       reimbursement2008 < 4990   to the right, improve=1.7636660, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=0.8701440, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.4025273, (0 missing)
##   Surrogate splits:
##       stroke            < 0.5    to the left,  agree=0.668, adj=0.09, (0 split)
##       reimbursement2008 < 3085   to the right, agree=0.642, adj=0.02, (0 split)
## 
## Node number 30905: 23 observations
##   predicted class=B2  expected loss=0.3913043  P(node) =8.369632e-05
##     class counts:     5    14     3     1     0
##    probabilities: 0.217 0.609 0.130 0.043 0.000 
## 
## Node number 30918: 721 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5492372  P(node) =0.002623698
##     class counts:   234   325   114    44     4
##    probabilities: 0.325 0.451 0.158 0.061 0.006 
##   left son=61836 (109 obs) right son=61837 (612 obs)
##   Primary splits:
##       age               < 86.5   to the right, improve=5.2024390, (0 missing)
##       reimbursement2008 < 8105   to the left,  improve=1.9497410, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.3441110, (0 missing)
##       bucket2008        < 2.5    to the right, improve=0.8592657, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.1415473, (0 missing)
## 
## Node number 30919: 332 observations
##   predicted class=B2  expected loss=0.4939759  P(node) =0.001208138
##     class counts:    81   168    61    20     2
##    probabilities: 0.244 0.506 0.184 0.060 0.006 
## 
## Node number 30962: 368 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.625  P(node) =0.001339141
##     class counts:   138   132    66    32     0
##    probabilities: 0.375 0.359 0.179 0.087 0.000 
##   left son=61924 (261 obs) right son=61925 (107 obs)
##   Primary splits:
##       reimbursement2008 < 10440  to the right, improve=2.0386870, (0 missing)
##       age               < 68.5   to the right, improve=2.0238320, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=1.0604210, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.8507150, (0 missing)
##       heart.failure     < 0.5    to the right, improve=0.3195541, (0 missing)
## 
## Node number 30963: 188 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.6489362  P(node) =0.0006841264
##     class counts:    66    53    50    17     2
##    probabilities: 0.351 0.282 0.266 0.090 0.011 
##   left son=61926 (135 obs) right son=61927 (53 obs)
##   Primary splits:
##       age               < 55.5   to the right, improve=1.3142350, (0 missing)
##       reimbursement2008 < 8995   to the left,  improve=1.1323620, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.7672950, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.7658279, (0 missing)
##       bucket2008        < 3.5    to the right, improve=0.3998270, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 8645   to the right, agree=0.723, adj=0.019, (0 split)
## 
## Node number 31768: 44 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5227273  P(node) =0.0001601147
##     class counts:    21    13     5     5     0
##    probabilities: 0.477 0.295 0.114 0.114 0.000 
##   left son=63536 (26 obs) right son=63537 (18 obs)
##   Primary splits:
##       reimbursement2008 < 9180   to the left,  improve=3.34188000, (0 missing)
##       age               < 73.5   to the right, improve=1.53473700, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=1.08333300, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.99564270, (0 missing)
##       copd              < 0.5    to the right, improve=0.09090909, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.864, adj=0.667, (0 split)
##       age        < 82.5   to the left,  agree=0.636, adj=0.111, (0 split)
##       stroke     < 0.5    to the left,  agree=0.614, adj=0.056, (0 split)
## 
## Node number 31769: 49 observations
##   predicted class=B2  expected loss=0.4897959  P(node) =0.0001783096
##     class counts:    11    25    10     3     0
##    probabilities: 0.224 0.510 0.204 0.061 0.000 
## 
## Node number 31848: 50 observations
##   predicted class=B2  expected loss=0.56  P(node) =0.0001819485
##     class counts:    21    22     1     6     0
##    probabilities: 0.420 0.440 0.020 0.120 0.000 
## 
## Node number 31849: 260 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.6923077  P(node) =0.0009461323
##     class counts:    80    77    63    31     9
##    probabilities: 0.308 0.296 0.242 0.119 0.035 
##   left son=63698 (20 obs) right son=63699 (240 obs)
##   Primary splits:
##       reimbursement2008 < 14765  to the right, improve=1.866026, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.724179, (0 missing)
##       age               < 59     to the right, improve=1.389622, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.186623, (0 missing)
##       stroke            < 0.5    to the right, improve=0.396978, (0 missing)
## 
## Node number 31850: 56 observations
##   predicted class=B2  expected loss=0.5178571  P(node) =0.0002037823
##     class counts:    11    27     9     9     0
##    probabilities: 0.196 0.482 0.161 0.161 0.000 
## 
## Node number 31851: 181 observations,    complexity param=5.534831e-05
##   predicted class=B3  expected loss=0.6519337  P(node) =0.0006586537
##     class counts:    42    54    63    19     3
##    probabilities: 0.232 0.298 0.348 0.105 0.017 
##   left son=63702 (136 obs) right son=63703 (45 obs)
##   Primary splits:
##       reimbursement2008 < 6865   to the right, improve=2.6510090, (0 missing)
##       age               < 95.5   to the left,  improve=1.1712710, (0 missing)
##       bucket2008        < 2.5    to the right, improve=0.4758931, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.1841866, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.1010412, (0 missing)
## 
## Node number 32560: 179 observations,    complexity param=7.379774e-05
##   predicted class=B1  expected loss=0.6815642  P(node) =0.0006513757
##     class counts:    57    51    27    31    13
##    probabilities: 0.318 0.285 0.151 0.173 0.073 
##   left son=65120 (38 obs) right son=65121 (141 obs)
##   Primary splits:
##       reimbursement2008 < 21440  to the right, improve=2.8400160, (0 missing)
##       age               < 70.5   to the left,  improve=1.0471050, (0 missing)
##       stroke            < 0.5    to the right, improve=0.8887163, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.8119666, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.6975642, (0 missing)
## 
## Node number 32561: 219 observations
##   predicted class=B4  expected loss=0.6894977  P(node) =0.0007969345
##     class counts:    53    50    36    68    12
##    probabilities: 0.242 0.228 0.164 0.311 0.055 
## 
## Node number 32562: 18 observations
##   predicted class=B1  expected loss=0.4444444  P(node) =6.550147e-05
##     class counts:    10     2     0     5     1
##    probabilities: 0.556 0.111 0.000 0.278 0.056 
## 
## Node number 32563: 41 observations
##   predicted class=B4  expected loss=0.6097561  P(node) =0.0001491978
##     class counts:    11     4    10    16     0
##    probabilities: 0.268 0.098 0.244 0.390 0.000 
## 
## Node number 55528: 117 observations,    complexity param=5.165842e-05
##   predicted class=B1  expected loss=0.4871795  P(node) =0.0004257595
##     class counts:    60    38    15     3     1
##    probabilities: 0.513 0.325 0.128 0.026 0.009 
##   left son=111056 (78 obs) right son=111057 (39 obs)
##   Primary splits:
##       reimbursement2008 < 2945   to the left,  improve=2.9829060, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.2210830, (0 missing)
##       age               < 76.5   to the right, improve=1.1210830, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.9103070, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.2543679, (0 missing)
##   Surrogate splits:
##       age < 76.5   to the right, agree=0.684, adj=0.051, (0 split)
## 
## Node number 55529: 226 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5663717  P(node) =0.0008224073
##     class counts:    92    98    22    13     1
##    probabilities: 0.407 0.434 0.097 0.058 0.004 
##   left son=111058 (72 obs) right son=111059 (154 obs)
##   Primary splits:
##       age               < 80.5   to the right, improve=1.5914710, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.3555880, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.7668454, (0 missing)
##       reimbursement2008 < 2795   to the left,  improve=0.6874895, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.4980787, (0 missing)
## 
## Node number 55532: 194 observations
##   predicted class=B1  expected loss=0.556701  P(node) =0.0007059603
##     class counts:    86    74    27     7     0
##    probabilities: 0.443 0.381 0.139 0.036 0.000 
## 
## Node number 55533: 24 observations
##   predicted class=B2  expected loss=0.375  P(node) =8.733529e-05
##     class counts:     7    15     1     1     0
##    probabilities: 0.292 0.625 0.042 0.042 0.000 
## 
## Node number 56004: 124 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5403226  P(node) =0.0004512323
##     class counts:    57    47    16     4     0
##    probabilities: 0.460 0.379 0.129 0.032 0.000 
##   left son=112008 (46 obs) right son=112009 (78 obs)
##   Primary splits:
##       age               < 72.5   to the right, improve=2.8817380, (0 missing)
##       reimbursement2008 < 2885   to the left,  improve=1.5254660, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.4454760, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.7103829, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.4023915, (0 missing)
##   Surrogate splits:
##       stroke            < 0.5    to the right, agree=0.661, adj=0.087, (0 split)
##       reimbursement2008 < 2575   to the left,  agree=0.645, adj=0.043, (0 split)
## 
## Node number 56005: 68 observations
##   predicted class=B2  expected loss=0.5441176  P(node) =0.00024745
##     class counts:    15    31    13     7     2
##    probabilities: 0.221 0.456 0.191 0.103 0.029 
## 
## Node number 56006: 8 observations
##   predicted class=B2  expected loss=0  P(node) =2.911176e-05
##     class counts:     0     8     0     0     0
##    probabilities: 0.000 1.000 0.000 0.000 0.000 
## 
## Node number 56007: 66 observations,    complexity param=5.534831e-05
##   predicted class=B3  expected loss=0.6060606  P(node) =0.0002401721
##     class counts:    24    14    26     2     0
##    probabilities: 0.364 0.212 0.394 0.030 0.000 
##   left son=112014 (40 obs) right son=112015 (26 obs)
##   Primary splits:
##       alzheimers        < 0.5    to the left,  improve=2.3576920, (0 missing)
##       age               < 65     to the left,  improve=1.8352940, (0 missing)
##       reimbursement2008 < 2375   to the right, improve=1.1494920, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=0.1363636, (0 missing)
##   Surrogate splits:
##       age < 82.5   to the left,  agree=0.652, adj=0.115, (0 split)
## 
## Node number 56716: 367 observations
##   predicted class=B1  expected loss=0.5395095  P(node) =0.001335502
##     class counts:   169   131    51    13     3
##    probabilities: 0.460 0.357 0.139 0.035 0.008 
## 
## Node number 56717: 322 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.5962733  P(node) =0.001171748
##     class counts:   126   130    41    25     0
##    probabilities: 0.391 0.404 0.127 0.078 0.000 
##   left son=113434 (78 obs) right son=113435 (244 obs)
##   Primary splits:
##       age               < 67.5   to the left,  improve=2.0124890, (0 missing)
##       reimbursement2008 < 2265   to the right, improve=1.1949400, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.3273471, (0 missing)
##       depression        < 0.5    to the right, improve=0.1786959, (0 missing)
##       copd              < 0.5    to the left,  improve=0.1745923, (0 missing)
## 
## Node number 61616: 245 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5346939  P(node) =0.0008915478
##     class counts:   114    87    27    16     1
##    probabilities: 0.465 0.355 0.110 0.065 0.004 
##   left son=123232 (209 obs) right son=123233 (36 obs)
##   Primary splits:
##       reimbursement2008 < 8170   to the left,  improve=1.7182870, (0 missing)
##       age               < 90.5   to the right, improve=1.6062760, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.7459219, (0 missing)
##       depression        < 0.5    to the left,  improve=0.6596720, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.6366849, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.971, adj=0.806, (0 split)
## 
## Node number 61617: 8 observations
##   predicted class=B3  expected loss=0.625  P(node) =2.911176e-05
##     class counts:     1     2     3     1     1
##    probabilities: 0.125 0.250 0.375 0.125 0.125 
## 
## Node number 61784: 759 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5678524  P(node) =0.002761979
##     class counts:   328   303    94    33     1
##    probabilities: 0.432 0.399 0.124 0.043 0.001 
##   left son=123568 (158 obs) right son=123569 (601 obs)
##   Primary splits:
##       reimbursement2008 < 4315   to the right, improve=1.62186500, (0 missing)
##       age               < 82.5   to the right, improve=0.60286370, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.24697950, (0 missing)
##       copd              < 0.5    to the left,  improve=0.10233690, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.09394217, (0 missing)
## 
## Node number 61785: 719 observations,    complexity param=9.962695e-05
##   predicted class=B2  expected loss=0.5757997  P(node) =0.00261642
##     class counts:   272   305   105    34     3
##    probabilities: 0.378 0.424 0.146 0.047 0.004 
##   left son=123570 (346 obs) right son=123571 (373 obs)
##   Primary splits:
##       reimbursement2008 < 5835   to the left,  improve=2.8015510, (0 missing)
##       age               < 59.5   to the right, improve=2.2849680, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.5855315, (0 missing)
##       stroke            < 0.5    to the right, improve=0.5109046, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.2469968, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.590, adj=0.147, (0 split)
##       alzheimers < 0.5    to the left,  agree=0.537, adj=0.038, (0 split)
##       age        < 60.5   to the left,  agree=0.527, adj=0.017, (0 split)
## 
## Node number 61802: 137 observations
##   predicted class=B1  expected loss=0.5839416  P(node) =0.000498539
##     class counts:    57    43    22    15     0
##    probabilities: 0.416 0.314 0.161 0.109 0.000 
## 
## Node number 61803: 268 observations
##   predicted class=B2  expected loss=0.6007463  P(node) =0.0009752441
##     class counts:    91   107    51    18     1
##    probabilities: 0.340 0.399 0.190 0.067 0.004 
## 
## Node number 61808: 174 observations
##   predicted class=B1  expected loss=0.5229885  P(node) =0.0006331809
##     class counts:    83    53    29     8     1
##    probabilities: 0.477 0.305 0.167 0.046 0.006 
## 
## Node number 61809: 100 observations,    complexity param=7.19528e-05
##   predicted class=B2  expected loss=0.54  P(node) =0.000363897
##     class counts:    38    46    13     3     0
##    probabilities: 0.380 0.460 0.130 0.030 0.000 
##   left son=123618 (26 obs) right son=123619 (74 obs)
##   Primary splits:
##       reimbursement2008 < 4355   to the right, improve=5.3372560, (0 missing)
##       age               < 62.5   to the right, improve=1.9704690, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.2703500, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.5886275, (0 missing)
##   Surrogate splits:
##       age < 50.5   to the left,  agree=0.79, adj=0.192, (0 split)
## 
## Node number 61836: 109 observations
##   predicted class=B1  expected loss=0.5412844  P(node) =0.0003966478
##     class counts:    50    33    16     9     1
##    probabilities: 0.459 0.303 0.147 0.083 0.009 
## 
## Node number 61837: 612 observations
##   predicted class=B2  expected loss=0.5228758  P(node) =0.00222705
##     class counts:   184   292    98    35     3
##    probabilities: 0.301 0.477 0.160 0.057 0.005 
## 
## Node number 61924: 261 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5862069  P(node) =0.0009497713
##     class counts:   108    87    44    22     0
##    probabilities: 0.414 0.333 0.169 0.084 0.000 
##   left son=123848 (92 obs) right son=123849 (169 obs)
##   Primary splits:
##       reimbursement2008 < 12585  to the left,  improve=2.1315740, (0 missing)
##       age               < 77.5   to the right, improve=1.2761660, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.0543160, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=1.0296720, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.5202291, (0 missing)
## 
## Node number 61925: 107 observations
##   predicted class=B2  expected loss=0.5794393  P(node) =0.0003893698
##     class counts:    30    45    22    10     0
##    probabilities: 0.280 0.421 0.206 0.093 0.000 
## 
## Node number 61926: 135 observations
##   predicted class=B1  expected loss=0.6074074  P(node) =0.000491261
##     class counts:    53    34    36    12     0
##    probabilities: 0.393 0.252 0.267 0.089 0.000 
## 
## Node number 61927: 53 observations
##   predicted class=B2  expected loss=0.6415094  P(node) =0.0001928654
##     class counts:    13    19    14     5     2
##    probabilities: 0.245 0.358 0.264 0.094 0.038 
## 
## Node number 63536: 26 observations
##   predicted class=B1  expected loss=0.3461538  P(node) =9.461323e-05
##     class counts:    17     4     2     3     0
##    probabilities: 0.654 0.154 0.077 0.115 0.000 
## 
## Node number 63537: 18 observations
##   predicted class=B2  expected loss=0.5  P(node) =6.550147e-05
##     class counts:     4     9     3     2     0
##    probabilities: 0.222 0.500 0.167 0.111 0.000 
## 
## Node number 63698: 20 observations
##   predicted class=B1  expected loss=0.45  P(node) =7.277941e-05
##     class counts:    11     5     2     1     1
##    probabilities: 0.550 0.250 0.100 0.050 0.050 
## 
## Node number 63699: 240 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.7  P(node) =0.0008733529
##     class counts:    69    72    61    30     8
##    probabilities: 0.288 0.300 0.254 0.125 0.033 
##   left son=127398 (201 obs) right son=127399 (39 obs)
##   Primary splits:
##       age               < 61.5   to the right, improve=1.4580460, (0 missing)
##       reimbursement2008 < 10970  to the right, improve=1.4206140, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.0755290, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.9752886, (0 missing)
##       stroke            < 0.5    to the right, improve=0.4524283, (0 missing)
## 
## Node number 63702: 136 observations
##   predicted class=B3  expected loss=0.6029412  P(node) =0.0004949
##     class counts:    34    36    54    10     2
##    probabilities: 0.250 0.265 0.397 0.074 0.015 
## 
## Node number 63703: 45 observations
##   predicted class=B2  expected loss=0.6  P(node) =0.0001637537
##     class counts:     8    18     9     9     1
##    probabilities: 0.178 0.400 0.200 0.200 0.022 
## 
## Node number 65120: 38 observations
##   predicted class=B2  expected loss=0.5  P(node) =0.0001382809
##     class counts:     9    19     4     5     1
##    probabilities: 0.237 0.500 0.105 0.132 0.026 
## 
## Node number 65121: 141 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.6595745  P(node) =0.0005130948
##     class counts:    48    32    23    26    12
##    probabilities: 0.340 0.227 0.163 0.184 0.085 
##   left son=130242 (89 obs) right son=130243 (52 obs)
##   Primary splits:
##       reimbursement2008 < 17585  to the right, improve=2.1889060, (0 missing)
##       age               < 47.5   to the right, improve=1.2186760, (0 missing)
##       bucket2008        < 3.5    to the right, improve=1.1702130, (0 missing)
##       stroke            < 0.5    to the right, improve=0.9175166, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.5919705, (0 missing)
##   Surrogate splits:
##       bucket2008 < 3.5    to the right, agree=0.702, adj=0.192, (0 split)
##       age        < 47.5   to the right, agree=0.667, adj=0.096, (0 split)
## 
## Node number 111056: 78 observations
##   predicted class=B1  expected loss=0.4102564  P(node) =0.0002838397
##     class counts:    46    19    11     2     0
##    probabilities: 0.590 0.244 0.141 0.026 0.000 
## 
## Node number 111057: 39 observations
##   predicted class=B2  expected loss=0.5128205  P(node) =0.0001419198
##     class counts:    14    19     4     1     1
##    probabilities: 0.359 0.487 0.103 0.026 0.026 
## 
## Node number 111058: 72 observations
##   predicted class=B1  expected loss=0.4861111  P(node) =0.0002620059
##     class counts:    37    29     5     1     0
##    probabilities: 0.514 0.403 0.069 0.014 0.000 
## 
## Node number 111059: 154 observations
##   predicted class=B2  expected loss=0.5519481  P(node) =0.0005604015
##     class counts:    55    69    17    12     1
##    probabilities: 0.357 0.448 0.110 0.078 0.006 
## 
## Node number 112008: 46 observations
##   predicted class=B1  expected loss=0.4347826  P(node) =0.0001673926
##     class counts:    26    10     8     2     0
##    probabilities: 0.565 0.217 0.174 0.043 0.000 
## 
## Node number 112009: 78 observations
##   predicted class=B2  expected loss=0.525641  P(node) =0.0002838397
##     class counts:    31    37     8     2     0
##    probabilities: 0.397 0.474 0.103 0.026 0.000 
## 
## Node number 112014: 40 observations
##   predicted class=B1  expected loss=0.6  P(node) =0.0001455588
##     class counts:    16    12    11     1     0
##    probabilities: 0.400 0.300 0.275 0.025 0.000 
## 
## Node number 112015: 26 observations
##   predicted class=B3  expected loss=0.4230769  P(node) =9.461323e-05
##     class counts:     8     2    15     1     0
##    probabilities: 0.308 0.077 0.577 0.038 0.000 
## 
## Node number 113434: 78 observations
##   predicted class=B1  expected loss=0.5512821  P(node) =0.0002838397
##     class counts:    35    23    15     5     0
##    probabilities: 0.449 0.295 0.192 0.064 0.000 
## 
## Node number 113435: 244 observations
##   predicted class=B2  expected loss=0.5614754  P(node) =0.0008879088
##     class counts:    91   107    26    20     0
##    probabilities: 0.373 0.439 0.107 0.082 0.000 
## 
## Node number 123232: 209 observations
##   predicted class=B1  expected loss=0.507177  P(node) =0.0007605448
##     class counts:   103    70    22    14     0
##    probabilities: 0.493 0.335 0.105 0.067 0.000 
## 
## Node number 123233: 36 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.5277778  P(node) =0.0001310029
##     class counts:    11    17     5     2     1
##    probabilities: 0.306 0.472 0.139 0.056 0.028 
##   left son=246466 (15 obs) right son=246467 (21 obs)
##   Primary splits:
##       age               < 74.5   to the left,  improve=5.3968250, (0 missing)
##       reimbursement2008 < 8705   to the right, improve=1.5053320, (0 missing)
##       copd              < 0.5    to the right, improve=0.3703704, (0 missing)
##       depression        < 0.5    to the right, improve=0.3527778, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.2972583, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 8460   to the left,  agree=0.611, adj=0.067, (0 split)
## 
## Node number 123568: 158 observations
##   predicted class=B1  expected loss=0.5  P(node) =0.0005749573
##     class counts:    79    55    15     8     1
##    probabilities: 0.500 0.348 0.095 0.051 0.006 
## 
## Node number 123569: 601 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5856905  P(node) =0.002187021
##     class counts:   249   248    79    25     0
##    probabilities: 0.414 0.413 0.131 0.042 0.000 
##   left son=247138 (592 obs) right son=247139 (9 obs)
##   Primary splits:
##       reimbursement2008 < 4295   to the left,  improve=3.0859230, (0 missing)
##       age               < 62.5   to the right, improve=0.8258296, (0 missing)
##       copd              < 0.5    to the left,  improve=0.2730143, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.1209200, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.1102521, (0 missing)
## 
## Node number 123570: 346 observations
##   predicted class=B2  expected loss=0.5202312  P(node) =0.001259084
##     class counts:   122   166    44    13     1
##    probabilities: 0.353 0.480 0.127 0.038 0.003 
## 
## Node number 123571: 373 observations,    complexity param=9.962695e-05
##   predicted class=B1  expected loss=0.5978552  P(node) =0.001357336
##     class counts:   150   139    61    21     2
##    probabilities: 0.402 0.373 0.164 0.056 0.005 
##   left son=247142 (124 obs) right son=247143 (249 obs)
##   Primary splits:
##       alzheimers        < 0.5    to the right, improve=1.9370400, (0 missing)
##       reimbursement2008 < 6045   to the right, improve=1.9317030, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=1.1351660, (0 missing)
##       age               < 68.5   to the left,  improve=0.9923350, (0 missing)
##       stroke            < 0.5    to the right, improve=0.8206414, (0 missing)
##   Surrogate splits:
##       age               < 64.5   to the left,  agree=0.673, adj=0.016, (0 split)
##       stroke            < 0.5    to the right, agree=0.673, adj=0.016, (0 split)
##       reimbursement2008 < 5845   to the left,  agree=0.670, adj=0.008, (0 split)
## 
## Node number 123618: 26 observations
##   predicted class=B1  expected loss=0.3846154  P(node) =9.461323e-05
##     class counts:    16     4     4     2     0
##    probabilities: 0.615 0.154 0.154 0.077 0.000 
## 
## Node number 123619: 74 observations
##   predicted class=B2  expected loss=0.4324324  P(node) =0.0002692838
##     class counts:    22    42     9     1     0
##    probabilities: 0.297 0.568 0.122 0.014 0.000 
## 
## Node number 123848: 92 observations
##   predicted class=B1  expected loss=0.5  P(node) =0.0003347853
##     class counts:    46    23    17     6     0
##    probabilities: 0.500 0.250 0.185 0.065 0.000 
## 
## Node number 123849: 169 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.6213018  P(node) =0.000614986
##     class counts:    62    64    27    16     0
##    probabilities: 0.367 0.379 0.160 0.095 0.000 
##   left son=247698 (109 obs) right son=247699 (60 obs)
##   Primary splits:
##       reimbursement2008 < 14485  to the right, improve=2.3703890, (0 missing)
##       age               < 77.5   to the right, improve=1.8205180, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.5605270, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.9473954, (0 missing)
##       stroke            < 0.5    to the right, improve=0.8779250, (0 missing)
## 
## Node number 127398: 201 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.6865672  P(node) =0.0007314331
##     class counts:    63    58    49    27     4
##    probabilities: 0.313 0.289 0.244 0.134 0.020 
##   left son=254796 (112 obs) right son=254797 (89 obs)
##   Primary splits:
##       reimbursement2008 < 12625  to the left,  improve=2.6465710, (0 missing)
##       age               < 72.5   to the right, improve=1.5701210, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.5204340, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.8281641, (0 missing)
##       stroke            < 0.5    to the right, improve=0.4454147, (0 missing)
##   Surrogate splits:
##       age < 67.5   to the right, agree=0.587, adj=0.067, (0 split)
## 
## Node number 127399: 39 observations
##   predicted class=B2  expected loss=0.6410256  P(node) =0.0001419198
##     class counts:     6    14    12     3     4
##    probabilities: 0.154 0.359 0.308 0.077 0.103 
## 
## Node number 130242: 89 observations
##   predicted class=B1  expected loss=0.5955056  P(node) =0.0003238684
##     class counts:    36    15    17    14     7
##    probabilities: 0.404 0.169 0.191 0.157 0.079 
## 
## Node number 130243: 52 observations
##   predicted class=B2  expected loss=0.6730769  P(node) =0.0001892265
##     class counts:    12    17     6    12     5
##    probabilities: 0.231 0.327 0.115 0.231 0.096 
## 
## Node number 246466: 15 observations
##   predicted class=B1  expected loss=0.4  P(node) =5.458456e-05
##     class counts:     9     2     3     0     1
##    probabilities: 0.600 0.133 0.200 0.000 0.067 
## 
## Node number 246467: 21 observations
##   predicted class=B2  expected loss=0.2857143  P(node) =7.641838e-05
##     class counts:     2    15     2     2     0
##    probabilities: 0.095 0.714 0.095 0.095 0.000 
## 
## Node number 247138: 592 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5810811  P(node) =0.002154271
##     class counts:   248   240    79    25     0
##    probabilities: 0.419 0.405 0.133 0.042 0.000 
##   left son=494276 (135 obs) right son=494277 (457 obs)
##   Primary splits:
##       age               < 82.5   to the right, improve=1.0162580, (0 missing)
##       reimbursement2008 < 3485   to the left,  improve=0.9533819, (0 missing)
##       copd              < 0.5    to the left,  improve=0.2603666, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.1489946, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.1384892, (0 missing)
## 
## Node number 247139: 9 observations
##   predicted class=B2  expected loss=0.1111111  P(node) =3.275073e-05
##     class counts:     1     8     0     0     0
##    probabilities: 0.111 0.889 0.000 0.000 0.000 
## 
## Node number 247142: 124 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.516129  P(node) =0.0004512323
##     class counts:    60    39    19     5     1
##    probabilities: 0.484 0.315 0.153 0.040 0.008 
##   left son=494284 (114 obs) right son=494285 (10 obs)
##   Primary splits:
##       reimbursement2008 < 8555   to the left,  improve=3.2894170, (0 missing)
##       age               < 62.5   to the right, improve=1.3134040, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=0.8306452, (0 missing)
##       stroke            < 0.5    to the right, improve=0.6624062, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.6169185, (0 missing)
## 
## Node number 247143: 249 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.5983936  P(node) =0.0009061036
##     class counts:    90   100    42    16     1
##    probabilities: 0.361 0.402 0.169 0.064 0.004 
##   left son=494286 (217 obs) right son=494287 (32 obs)
##   Primary splits:
##       reimbursement2008 < 6045   to the right, improve=2.8382200, (0 missing)
##       age               < 68.5   to the left,  improve=1.5757780, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.8580882, (0 missing)
##       copd              < 0.5    to the left,  improve=0.4427711, (0 missing)
##       stroke            < 0.5    to the right, improve=0.2244234, (0 missing)
## 
## Node number 247698: 109 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5779817  P(node) =0.0003966478
##     class counts:    46    34    19    10     0
##    probabilities: 0.422 0.312 0.174 0.092 0.000 
##   left son=495396 (72 obs) right son=495397 (37 obs)
##   Primary splits:
##       osteoporosis      < 0.5    to the left,  improve=2.1824740, (0 missing)
##       reimbursement2008 < 17235  to the right, improve=1.3957040, (0 missing)
##       age               < 70.5   to the left,  improve=1.2827700, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.2406940, (0 missing)
##       bucket2008        < 3.5    to the left,  improve=0.2455781, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 14660  to the right, agree=0.679, adj=0.054, (0 split)
## 
## Node number 247699: 60 observations
##   predicted class=B2  expected loss=0.5  P(node) =0.0002183382
##     class counts:    16    30     8     6     0
##    probabilities: 0.267 0.500 0.133 0.100 0.000 
## 
## Node number 254796: 112 observations
##   predicted class=B1  expected loss=0.6160714  P(node) =0.0004075647
##     class counts:    43    27    31    10     1
##    probabilities: 0.384 0.241 0.277 0.089 0.009 
## 
## Node number 254797: 89 observations
##   predicted class=B2  expected loss=0.6516854  P(node) =0.0003238684
##     class counts:    20    31    18    17     3
##    probabilities: 0.225 0.348 0.202 0.191 0.034 
## 
## Node number 494276: 135 observations
##   predicted class=B1  expected loss=0.5259259  P(node) =0.000491261
##     class counts:    64    49    20     2     0
##    probabilities: 0.474 0.363 0.148 0.015 0.000 
## 
## Node number 494277: 457 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.5820569  P(node) =0.00166301
##     class counts:   184   191    59    23     0
##    probabilities: 0.403 0.418 0.129 0.050 0.000 
##   left son=988554 (290 obs) right son=988555 (167 obs)
##   Primary splits:
##       age               < 74.5   to the left,  improve=0.9874503, (0 missing)
##       reimbursement2008 < 3495   to the left,  improve=0.9861916, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.3272674, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.2337493, (0 missing)
##       copd              < 0.5    to the left,  improve=0.1994562, (0 missing)
##   Surrogate splits:
##       alzheimers < 0.5    to the left,  agree=0.637, adj=0.006, (0 split)
## 
## Node number 494284: 114 observations
##   predicted class=B1  expected loss=0.4824561  P(node) =0.0004148426
##     class counts:    59    32    18     4     1
##    probabilities: 0.518 0.281 0.158 0.035 0.009 
## 
## Node number 494285: 10 observations
##   predicted class=B2  expected loss=0.3  P(node) =3.63897e-05
##     class counts:     1     7     1     1     0
##    probabilities: 0.100 0.700 0.100 0.100 0.000 
## 
## Node number 494286: 217 observations
##   predicted class=B2  expected loss=0.5714286  P(node) =0.0007896566
##     class counts:    78    93    30    15     1
##    probabilities: 0.359 0.429 0.138 0.069 0.005 
## 
## Node number 494287: 32 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.625  P(node) =0.0001164471
##     class counts:    12     7    12     1     0
##    probabilities: 0.375 0.219 0.375 0.031 0.000 
##   left son=988574 (11 obs) right son=988575 (21 obs)
##   Primary splits:
##       age               < 72.5   to the left,  improve=1.8097940, (0 missing)
##       reimbursement2008 < 5975   to the left,  improve=0.7232143, (0 missing)
##       copd              < 0.5    to the left,  improve=0.6875000, (0 missing)
## 
## Node number 495396: 72 observations
##   predicted class=B1  expected loss=0.5138889  P(node) =0.0002620059
##     class counts:    35    17    12     8     0
##    probabilities: 0.486 0.236 0.167 0.111 0.000 
## 
## Node number 495397: 37 observations
##   predicted class=B2  expected loss=0.5405405  P(node) =0.0001346419
##     class counts:    11    17     7     2     0
##    probabilities: 0.297 0.459 0.189 0.054 0.000 
## 
## Node number 988554: 290 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5724138  P(node) =0.001055301
##     class counts:   124   114    37    15     0
##    probabilities: 0.428 0.393 0.128 0.052 0.000 
##   left son=1977108 (234 obs) right son=1977109 (56 obs)
##   Primary splits:
##       age               < 62.5   to the right, improve=1.0825800, (0 missing)
##       reimbursement2008 < 3945   to the right, improve=0.7040408, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.6026089, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.2655768, (0 missing)
##       copd              < 0.5    to the left,  improve=0.1804923, (0 missing)
## 
## Node number 988555: 167 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.5389222  P(node) =0.0006077081
##     class counts:    60    77    22     8     0
##    probabilities: 0.359 0.461 0.132 0.048 0.000 
##   left son=1977110 (39 obs) right son=1977111 (128 obs)
##   Primary splits:
##       reimbursement2008 < 4105   to the right, improve=1.3886510, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.7439669, (0 missing)
##       age               < 81.5   to the left,  improve=0.4824922, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.2060442, (0 missing)
##       copd              < 0.5    to the left,  improve=0.1297289, (0 missing)
## 
## Node number 988574: 11 observations
##   predicted class=B1  expected loss=0.3636364  P(node) =4.002868e-05
##     class counts:     7     2     2     0     0
##    probabilities: 0.636 0.182 0.182 0.000 0.000 
## 
## Node number 988575: 21 observations
##   predicted class=B3  expected loss=0.5238095  P(node) =7.641838e-05
##     class counts:     5     5    10     1     0
##    probabilities: 0.238 0.238 0.476 0.048 0.000 
## 
## Node number 1977108: 234 observations
##   predicted class=B1  expected loss=0.5470085  P(node) =0.0008515191
##     class counts:   106    89    28    11     0
##    probabilities: 0.453 0.380 0.120 0.047 0.000 
## 
## Node number 1977109: 56 observations
##   predicted class=B2  expected loss=0.5535714  P(node) =0.0002037823
##     class counts:    18    25     9     4     0
##    probabilities: 0.321 0.446 0.161 0.071 0.000 
## 
## Node number 1977110: 39 observations
##   predicted class=B1  expected loss=0.5128205  P(node) =0.0001419198
##     class counts:    19    14     5     1     0
##    probabilities: 0.487 0.359 0.128 0.026 0.000 
## 
## Node number 1977111: 128 observations
##   predicted class=B2  expected loss=0.5078125  P(node) =0.0004657882
##     class counts:    41    63    17     7     0
##    probabilities: 0.320 0.492 0.133 0.055 0.000 
## 
## n= 274803 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
##       1) root 274803 90337 B1 (0.67 0.19 0.089 0.043 0.0058)  
##         2) reimbursement2008< 1565 165987 20938 B1 (0.87 0.074 0.037 0.014 0.0014) *
##         3) reimbursement2008>=1565 108816 68841 B2 (0.36 0.37 0.17 0.088 0.012)  
##           6) reimbursement2008< 3065 39298 18853 B1 (0.52 0.31 0.12 0.045 0.0046)  
##            12) reimbursement2008< 2175 20077  8527 B1 (0.58 0.27 0.11 0.042 0.0038)  
##              24) diabetes< 0.5 8826  3280 B1 (0.63 0.24 0.091 0.035 0.0029) *
##              25) diabetes>=0.5 11251  5247 B1 (0.53 0.29 0.12 0.046 0.0045)  
##                50) kidney< 0.5 9007  4045 B1 (0.55 0.28 0.12 0.042 0.0044)  
##                 100) reimbursement2008< 1875 4935  2071 B1 (0.58 0.26 0.11 0.042 0.0045) *
##                 101) reimbursement2008>=1875 4072  1974 B1 (0.52 0.31 0.13 0.042 0.0044)  
##                   202) cancer< 0.5 3786  1807 B1 (0.52 0.3 0.13 0.041 0.0048) *
##                   203) cancer>=0.5 286   167 B1 (0.42 0.4 0.13 0.056 0)  
##                     406) age< 73.5 128    64 B1 (0.5 0.33 0.11 0.062 0)  
##                       812) depression< 0.5 95    41 B1 (0.57 0.26 0.12 0.053 0) *
##                       813) depression>=0.5 33    16 B2 (0.3 0.52 0.091 0.091 0) *
##                     407) age>=73.5 158    85 B2 (0.35 0.46 0.14 0.051 0) *
##                51) kidney>=0.5 2244  1202 B1 (0.46 0.33 0.14 0.064 0.0049)  
##                 102) heart.failure< 0.5 992   477 B1 (0.52 0.29 0.13 0.057 0.002) *
##                 103) heart.failure>=0.5 1252   725 B1 (0.42 0.36 0.15 0.069 0.0072)  
##                   206) arthritis< 0.5 904   486 B1 (0.46 0.34 0.13 0.063 0.0066)  
##                     412) reimbursement2008< 1735 270   125 B1 (0.54 0.27 0.13 0.056 0.0037) *
##                     413) reimbursement2008>=1735 634   361 B1 (0.43 0.36 0.13 0.066 0.0079)  
##                       826) age< 91.5 596   330 B1 (0.45 0.36 0.13 0.057 0.0084)  
##                        1652) reimbursement2008>=1765 555   302 B1 (0.46 0.35 0.14 0.052 0.0072)  
##                          3304) reimbursement2008< 1955 265   130 B1 (0.51 0.31 0.13 0.049 0.0038)  
##                            6608) stroke< 0.5 251   118 B1 (0.53 0.29 0.13 0.044 0.004)  
##                             13216) cancer< 0.5 235   106 B1 (0.55 0.27 0.13 0.047 0.0043) *
##                             13217) cancer>=0.5 16     7 B2 (0.25 0.56 0.19 0 0) *
##                            6609) stroke>=0.5 14     5 B2 (0.14 0.64 0.071 0.14 0) *
##                          3305) reimbursement2008>=1955 290   172 B1 (0.41 0.38 0.14 0.055 0.01)  
##                            6610) age< 81.5 213   120 B1 (0.44 0.35 0.14 0.07 0.0047)  
##                             13220) age>=44.5 201   110 B1 (0.45 0.33 0.14 0.07 0) *
##                             13221) age< 44.5 12     5 B2 (0.17 0.58 0.083 0.083 0.083) *
##                            6611) age>=81.5 77    40 B2 (0.32 0.48 0.16 0.013 0.026) *
##                        1653) reimbursement2008< 1765 41    20 B2 (0.32 0.51 0.024 0.12 0.024) *
##                       827) age>=91.5 38    21 B2 (0.18 0.45 0.16 0.21 0) *
##                   207) arthritis>=0.5 348   205 B2 (0.31 0.41 0.18 0.086 0.0086) *
##            13) reimbursement2008>=2175 19221 10326 B1 (0.46 0.35 0.13 0.049 0.0054)  
##              26) diabetes< 0.5 7137  3360 B1 (0.53 0.31 0.11 0.042 0.0046)  
##                52) arthritis< 0.5 5554  2471 B1 (0.56 0.3 0.1 0.039 0.0049)  
##                 104) ihd< 0.5 2348   933 B1 (0.6 0.27 0.092 0.031 0.0051) *
##                 105) ihd>=0.5 3206  1538 B1 (0.52 0.32 0.11 0.045 0.0047)  
##                   210) depression< 0.5 2325  1056 B1 (0.55 0.3 0.11 0.043 0.0052) *
##                   211) depression>=0.5 881   482 B1 (0.45 0.36 0.13 0.052 0.0034)  
##                     422) kidney< 0.5 763   405 B1 (0.47 0.34 0.13 0.052 0.0039) *
##                     423) kidney>=0.5 118    63 B2 (0.35 0.47 0.14 0.051 0)  
##                       846) reimbursement2008>=2865 22    10 B1 (0.55 0.23 0.18 0.045 0) *
##                       847) reimbursement2008< 2865 96    46 B2 (0.3 0.52 0.12 0.052 0) *
##                53) arthritis>=0.5 1583   889 B1 (0.44 0.37 0.14 0.052 0.0038)  
##                 106) stroke< 0.5 1525   844 B1 (0.45 0.36 0.13 0.054 0.0039)  
##                   212) cancer< 0.5 1438   784 B1 (0.45 0.36 0.13 0.053 0.0042)  
##                     424) reimbursement2008>=2715 495   280 B1 (0.43 0.41 0.1 0.053 0.004)  
##                       848) reimbursement2008>=2795 385   210 B1 (0.45 0.38 0.1 0.06 0.0052)  
##                        1696) age< 80.5 263   131 B1 (0.5 0.36 0.099 0.042 0) *
##                        1697) age>=80.5 122    70 B2 (0.35 0.43 0.11 0.098 0.016) *
##                       849) reimbursement2008< 2795 110    54 B2 (0.36 0.51 0.1 0.027 0) *
##                     425) reimbursement2008< 2715 943   504 B1 (0.47 0.33 0.15 0.053 0.0042) *
##                   213) cancer>=0.5 87    48 B2 (0.31 0.45 0.17 0.069 0) *
##                 107) stroke>=0.5 58    26 B2 (0.22 0.55 0.21 0.017 0) *
##              27) diabetes>=0.5 12084  6966 B1 (0.42 0.37 0.15 0.054 0.0059)  
##                54) arthritis< 0.5 8413  4653 B1 (0.45 0.35 0.15 0.052 0.0056)  
##                 108) heart.failure< 0.5 4375  2220 B1 (0.49 0.34 0.13 0.039 0.0039)  
##                   216) cancer< 0.5 3992  1978 B1 (0.5 0.33 0.12 0.038 0.0033)  
##                     432) ihd< 0.5 1265   562 B1 (0.56 0.29 0.12 0.035 0.0032) *
##                     433) ihd>=0.5 2727  1416 B1 (0.48 0.35 0.13 0.04 0.0033)  
##                       866) reimbursement2008< 2615 1499   736 B1 (0.51 0.33 0.13 0.035 0.002) *
##                       867) reimbursement2008>=2615 1228   680 B1 (0.45 0.37 0.13 0.046 0.0049)  
##                        1734) reimbursement2008>=2995 171    81 B1 (0.53 0.27 0.14 0.058 0.0058) *
##                        1735) reimbursement2008< 2995 1057   599 B1 (0.43 0.39 0.13 0.044 0.0047)  
##                          3470) age< 83.5 840   458 B1 (0.45 0.37 0.12 0.049 0.006)  
##                            6940) age< 54.5 71    31 B1 (0.56 0.23 0.15 0.042 0.014) *
##                            6941) age>=54.5 769   427 B1 (0.44 0.38 0.12 0.049 0.0052)  
##                             13882) age>=70.5 472   249 B1 (0.47 0.35 0.12 0.053 0.0085)  
##                               27764) age>=73.5 343   191 B1 (0.44 0.4 0.11 0.047 0.0058)  
##                                 55528) reimbursement2008>=2835 117    57 B1 (0.51 0.32 0.13 0.026 0.0085)  
##                                  111056) reimbursement2008< 2945 78    32 B1 (0.59 0.24 0.14 0.026 0) *
##                                  111057) reimbursement2008>=2945 39    20 B2 (0.36 0.49 0.1 0.026 0.026) *
##                                 55529) reimbursement2008< 2835 226   128 B2 (0.41 0.43 0.097 0.058 0.0044)  
##                                  111058) age>=80.5 72    35 B1 (0.51 0.4 0.069 0.014 0) *
##                                  111059) age< 80.5 154    85 B2 (0.36 0.45 0.11 0.078 0.0065) *
##                               27765) age< 73.5 129    58 B1 (0.55 0.21 0.16 0.07 0.016) *
##                             13883) age< 70.5 297   167 B2 (0.4 0.44 0.12 0.044 0)  
##                               27766) osteoporosis< 0.5 218   125 B1 (0.43 0.41 0.13 0.037 0)  
##                                 55532) reimbursement2008< 2945 194   108 B1 (0.44 0.38 0.14 0.036 0) *
##                                 55533) reimbursement2008>=2945 24     9 B2 (0.29 0.62 0.042 0.042 0) *
##                               27767) osteoporosis>=0.5 79    38 B2 (0.33 0.52 0.089 0.063 0) *
##                          3471) age>=83.5 217   116 B2 (0.35 0.47 0.16 0.028 0) *
##                   217) cancer>=0.5 383   220 B2 (0.37 0.43 0.15 0.044 0.01)  
##                     434) reimbursement2008< 2705 238   136 B1 (0.43 0.36 0.16 0.038 0.013)  
##                       868) depression< 0.5 167    84 B1 (0.5 0.3 0.15 0.042 0.012) *
##                       869) depression>=0.5 71    35 B2 (0.27 0.51 0.18 0.028 0.014) *
##                     435) reimbursement2008>=2705 145    68 B2 (0.27 0.53 0.14 0.055 0.0069) *
##                 109) heart.failure>=0.5 4038  2433 B1 (0.4 0.36 0.17 0.066 0.0074)  
##                   218) kidney< 0.5 2819  1620 B1 (0.43 0.35 0.16 0.065 0.0064)  
##                     436) ihd< 0.5 635   319 B1 (0.5 0.31 0.15 0.041 0.0063) *
##                     437) ihd>=0.5 2184  1301 B1 (0.4 0.36 0.16 0.072 0.0064)  
##                       874) reimbursement2008< 2315 393   202 B1 (0.49 0.34 0.12 0.051 0.0051) *
##                       875) reimbursement2008>=2315 1791  1099 B1 (0.39 0.36 0.17 0.076 0.0067)  
##                        1750) age>=39.5 1752  1066 B1 (0.39 0.36 0.17 0.075 0.0068)  
##                          3500) depression< 0.5 1099   639 B1 (0.42 0.35 0.15 0.069 0.0064)  
##                            7000) age< 95.5 1074   620 B1 (0.42 0.35 0.15 0.069 0.0065)  
##                             14000) copd< 0.5 808   450 B1 (0.44 0.34 0.14 0.075 0.0062) *
##                             14001) copd>=0.5 266   166 B2 (0.36 0.38 0.21 0.049 0.0075)  
##                               28002) reimbursement2008>=2540 192   114 B2 (0.38 0.41 0.15 0.057 0.01)  
##                                 56004) age< 78.5 124    67 B1 (0.46 0.38 0.13 0.032 0)  
##                                  112008) age>=72.5 46    20 B1 (0.57 0.22 0.17 0.043 0) *
##                                  112009) age< 72.5 78    41 B2 (0.4 0.47 0.1 0.026 0) *
##                                 56005) age>=78.5 68    37 B2 (0.22 0.46 0.19 0.1 0.029) *
##                               28003) reimbursement2008< 2540 74    48 B3 (0.32 0.3 0.35 0.027 0)  
##                                 56006) cancer>=0.5 8     0 B2 (0 1 0 0 0) *
##                                 56007) cancer< 0.5 66    40 B3 (0.36 0.21 0.39 0.03 0)  
##                                  112014) alzheimers< 0.5 40    24 B1 (0.4 0.3 0.27 0.025 0) *
##                                  112015) alzheimers>=0.5 26    11 B3 (0.31 0.077 0.58 0.038 0) *
##                            7001) age>=95.5 25    10 B2 (0.24 0.6 0.08 0.08 0) *
##                          3501) depression>=0.5 653   412 B2 (0.35 0.37 0.19 0.084 0.0077)  
##                            7002) reimbursement2008< 2655 303   183 B1 (0.4 0.33 0.2 0.069 0.0033) *
##                            7003) reimbursement2008>=2655 350   208 B2 (0.3 0.41 0.18 0.097 0.011) *
##                        1751) age< 39.5 39    18 B2 (0.15 0.54 0.15 0.15 0) *
##                   219) kidney>=0.5 1219   734 B2 (0.33 0.4 0.19 0.07 0.0098)  
##                     438) reimbursement2008< 2615 613   379 B1 (0.38 0.37 0.18 0.059 0.0098)  
##                       876) osteoporosis>=0.5 180   102 B1 (0.43 0.38 0.13 0.061 0)  
##                        1752) reimbursement2008< 2455 112    56 B1 (0.5 0.29 0.12 0.08 0) *
##                        1753) reimbursement2008>=2455 68    33 B2 (0.32 0.51 0.13 0.029 0) *
##                       877) osteoporosis< 0.5 433   275 B2 (0.36 0.36 0.2 0.058 0.014)  
##                        1754) stroke< 0.5 403   252 B1 (0.37 0.36 0.2 0.05 0.015)  
##                          3508) reimbursement2008< 2585 382   238 B2 (0.37 0.38 0.19 0.047 0.01)  
##                            7016) depression< 0.5 229   136 B1 (0.41 0.36 0.19 0.035 0.0087)  
##                             14032) cancer>=0.5 15     5 B1 (0.67 0.13 0.2 0 0) *
##                             14033) cancer< 0.5 214   131 B1 (0.39 0.37 0.19 0.037 0.0093)  
##                               28066) reimbursement2008< 2515 169    98 B1 (0.42 0.34 0.19 0.036 0.012) *
##                               28067) reimbursement2008>=2515 45    23 B2 (0.27 0.49 0.2 0.044 0) *
##                            7017) depression>=0.5 153    91 B2 (0.32 0.41 0.2 0.065 0.013)  
##                             14034) reimbursement2008>=2545 14     5 B1 (0.64 0.14 0.14 0.071 0) *
##                             14035) reimbursement2008< 2545 139    79 B2 (0.29 0.43 0.2 0.065 0.014) *
##                          3509) reimbursement2008>=2585 21    12 B1 (0.43 0.14 0.24 0.095 0.095) *
##                        1755) stroke>=0.5 30    19 B2 (0.17 0.37 0.3 0.17 0) *
##                     439) reimbursement2008>=2615 606   347 B2 (0.28 0.43 0.2 0.081 0.0099) *
##                55) arthritis>=0.5 3671  2129 B2 (0.37 0.42 0.15 0.057 0.0065)  
##                 110) reimbursement2008< 2665 2068  1224 B1 (0.41 0.4 0.14 0.057 0.0048)  
##                   220) ihd< 0.5 517   274 B1 (0.47 0.37 0.11 0.048 0.0019)  
##                     440) reimbursement2008< 2295 143    57 B1 (0.6 0.26 0.077 0.063 0) *
##                     441) reimbursement2008>=2295 374   217 B1 (0.42 0.41 0.12 0.043 0.0027)  
##                       882) reimbursement2008< 2315 25     6 B2 (0.24 0.76 0 0 0) *
##                       883) reimbursement2008>=2315 349   198 B1 (0.43 0.39 0.13 0.046 0.0029)  
##                        1766) cancer< 0.5 336   186 B1 (0.45 0.38 0.13 0.042 0)  
##                          3532) age< 90.5 322   176 B1 (0.45 0.37 0.13 0.043 0) *
##                          3533) age>=90.5 14     5 B2 (0.29 0.64 0.071 0 0) *
##                        1767) cancer>=0.5 13     6 B2 (0.077 0.54 0.15 0.15 0.077) *
##                   221) ihd>=0.5 1551   925 B2 (0.39 0.4 0.14 0.059 0.0058)  
##                     442) age< 35 18     5 B1 (0.72 0.22 0 0.056 0) *
##                     443) age>=35 1533   911 B2 (0.38 0.41 0.15 0.059 0.0059)  
##                       886) kidney< 0.5 1101   656 B1 (0.4 0.4 0.14 0.052 0.0045)  
##                        1772) stroke< 0.5 1057   623 B1 (0.41 0.4 0.14 0.051 0.0038)  
##                          3544) cancer< 0.5 1008   590 B1 (0.41 0.4 0.13 0.053 0.004)  
##                            7088) reimbursement2008>=2535 275   154 B2 (0.39 0.44 0.12 0.04 0.0036)  
##                             14176) age< 63.5 44    17 B2 (0.32 0.61 0.045 0.023 0) *
##                             14177) age>=63.5 231   137 B1 (0.41 0.41 0.14 0.043 0.0043)  
##                               28354) alzheimers< 0.5 169    95 B1 (0.44 0.36 0.15 0.047 0.0059) *
##                               28355) alzheimers>=0.5 62    28 B2 (0.32 0.55 0.097 0.032 0) *
##                            7089) reimbursement2008< 2535 733   423 B1 (0.42 0.38 0.14 0.057 0.0041)  
##                             14178) age>=97.5 10     3 B1 (0.7 0.2 0.1 0 0) *
##                             14179) age< 97.5 723   420 B1 (0.42 0.38 0.14 0.058 0.0041)  
##                               28358) age< 90.5 689   394 B1 (0.43 0.38 0.13 0.055 0.0044)  
##                                 56716) heart.failure< 0.5 367   198 B1 (0.46 0.36 0.14 0.035 0.0082) *
##                                 56717) heart.failure>=0.5 322   192 B2 (0.39 0.4 0.13 0.078 0)  
##                                  113434) age< 67.5 78    43 B1 (0.45 0.29 0.19 0.064 0) *
##                                  113435) age>=67.5 244   137 B2 (0.37 0.44 0.11 0.082 0) *
##                               28359) age>=90.5 34    18 B2 (0.24 0.47 0.18 0.12 0) *
##                          3545) cancer>=0.5 49    29 B2 (0.33 0.41 0.24 0.02 0) *
##                        1773) stroke>=0.5 44    20 B2 (0.25 0.55 0.11 0.068 0.023) *
##                       887) kidney>=0.5 432   254 B2 (0.33 0.41 0.17 0.079 0.0093)  
##                        1774) reimbursement2008>=2215 403   232 B2 (0.32 0.42 0.17 0.069 0.0099) *
##                        1775) reimbursement2008< 2215 29    16 B1 (0.45 0.24 0.1 0.21 0) *
##                 111) reimbursement2008>=2665 1603   878 B2 (0.32 0.45 0.16 0.058 0.0087) *
##           7) reimbursement2008>=3065 69518 41677 B2 (0.27 0.4 0.2 0.11 0.017)  
##            14) diabetes< 0.5 15717  8966 B1 (0.43 0.35 0.15 0.064 0.0071)  
##              28) cancer< 0.5 13123  7034 B1 (0.46 0.34 0.13 0.058 0.0065)  
##                56) arthritis< 0.5 9625  4692 B1 (0.51 0.31 0.12 0.054 0.0058)  
##                 112) ihd< 0.5 3135  1246 B1 (0.6 0.26 0.095 0.036 0.0032)  
##                   224) depression< 0.5 2292   821 B1 (0.64 0.24 0.08 0.034 0.0044) *
##                   225) depression>=0.5 843   425 B1 (0.5 0.33 0.14 0.04 0)  
##                     450) age< 92.5 810   398 B1 (0.51 0.32 0.13 0.041 0)  
##                       900) reimbursement2008>=11525 117    40 B1 (0.66 0.21 0.068 0.06 0) *
##                       901) reimbursement2008< 11525 693   358 B1 (0.48 0.33 0.14 0.038 0)  
##                        1802) reimbursement2008< 11105 684   352 B1 (0.49 0.34 0.14 0.038 0)  
##                          3604) reimbursement2008< 4365 286   134 B1 (0.53 0.33 0.12 0.017 0) *
##                          3605) reimbursement2008>=4365 398   218 B1 (0.45 0.34 0.15 0.053 0)  
##                            7210) reimbursement2008>=4700 340   173 B1 (0.49 0.31 0.14 0.053 0) *
##                            7211) reimbursement2008< 4700 58    28 B2 (0.22 0.52 0.21 0.052 0) *
##                        1803) reimbursement2008>=11105 9     4 B3 (0.33 0.11 0.56 0 0) *
##                     451) age>=92.5 33    14 B2 (0.18 0.58 0.21 0.03 0) *
##                 113) ihd>=0.5 6490  3446 B1 (0.47 0.33 0.13 0.063 0.0071)  
##                   226) depression< 0.5 4266  2110 B1 (0.51 0.31 0.12 0.056 0.0061)  
##                     452) osteoporosis< 0.5 3304  1572 B1 (0.52 0.3 0.12 0.055 0.0064)  
##                       904) reimbursement2008>=5905 1626   714 B1 (0.56 0.25 0.12 0.061 0.0068) *
##                       905) reimbursement2008< 5905 1678   858 B1 (0.49 0.34 0.12 0.05 0.006)  
##                        1810) reimbursement2008< 5695 1608   814 B1 (0.49 0.33 0.12 0.051 0.0062) *
##                        1811) reimbursement2008>=5695 70    34 B2 (0.37 0.51 0.086 0.029 0) *
##                     453) osteoporosis>=0.5 962   538 B1 (0.44 0.38 0.12 0.057 0.0052)  
##                       906) stroke< 0.5 857   465 B1 (0.46 0.37 0.12 0.056 0.0047)  
##                        1812) heart.failure< 0.5 405   203 B1 (0.5 0.35 0.1 0.044 0.0074)  
##                          3624) age< 83.5 329   159 B1 (0.52 0.32 0.11 0.049 0.0091) *
##                          3625) age>=83.5 76    41 B2 (0.42 0.46 0.092 0.026 0)  
##                            7250) reimbursement2008>=6785 21     7 B1 (0.67 0.24 0.095 0 0) *
##                            7251) reimbursement2008< 6785 55    25 B2 (0.33 0.55 0.091 0.036 0) *
##                        1813) heart.failure>=0.5 452   262 B1 (0.42 0.38 0.13 0.066 0.0022)  
##                          3626) reimbursement2008>=3875 362   201 B1 (0.44 0.35 0.13 0.069 0.0028) *
##                          3627) reimbursement2008< 3875 90    45 B2 (0.32 0.5 0.12 0.056 0)  
##                            7254) age< 69.5 21     9 B1 (0.57 0.29 0.048 0.095 0) *
##                            7255) age>=69.5 69    30 B2 (0.25 0.57 0.14 0.043 0) *
##                       907) stroke>=0.5 105    54 B2 (0.3 0.49 0.13 0.067 0.0095) *
##                   227) depression>=0.5 2224  1336 B1 (0.4 0.35 0.16 0.076 0.009)  
##                     454) kidney< 0.5 1518   863 B1 (0.43 0.34 0.16 0.061 0.0053) *
##                     455) kidney>=0.5 706   440 B2 (0.33 0.38 0.17 0.11 0.017)  
##                       910) reimbursement2008>=3155 696   431 B2 (0.33 0.38 0.16 0.11 0.017)  
##                        1820) heart.failure< 0.5 177    99 B1 (0.44 0.35 0.15 0.062 0) *
##                        1821) heart.failure>=0.5 519   316 B2 (0.3 0.39 0.17 0.12 0.023) *
##                       911) reimbursement2008< 3155 10     4 B3 (0.1 0.1 0.6 0.2 0) *
##                57) arthritis>=0.5 3498  2017 B2 (0.33 0.42 0.17 0.069 0.0083)  
##                 114) reimbursement2008< 8525 2340  1270 B2 (0.31 0.46 0.17 0.062 0.0064)  
##                   228) reimbursement2008< 4645 1359   754 B2 (0.34 0.45 0.15 0.056 0.0059)  
##                     456) ihd< 0.5 440   248 B2 (0.4 0.44 0.11 0.045 0.0045)  
##                       912) reimbursement2008< 3155 58    22 B2 (0.34 0.62 0 0.017 0.017) *
##                       913) reimbursement2008>=3155 382   225 B1 (0.41 0.41 0.13 0.05 0.0026)  
##                        1826) reimbursement2008< 3245 25     8 B1 (0.68 0.28 0.04 0 0) *
##                        1827) reimbursement2008>=3245 357   208 B2 (0.39 0.42 0.13 0.053 0.0028)  
##                          3654) age>=80.5 91    46 B1 (0.49 0.34 0.099 0.066 0) *
##                          3655) age< 80.5 266   148 B2 (0.36 0.44 0.15 0.049 0.0038) *
##                     457) ihd>=0.5 919   506 B2 (0.32 0.45 0.17 0.061 0.0065) *
##                   229) reimbursement2008>=4645 981   516 B2 (0.26 0.47 0.19 0.069 0.0071) *
##                 115) reimbursement2008>=8525 1158   722 B1 (0.38 0.35 0.17 0.085 0.012)  
##                   230) copd< 0.5 714   396 B1 (0.45 0.33 0.13 0.085 0.007)  
##                     460) depression< 0.5 412   196 B1 (0.52 0.29 0.1 0.073 0.0097) *
##                     461) depression>=0.5 302   183 B2 (0.34 0.39 0.16 0.1 0.0033)  
##                       922) age>=92.5 9     3 B1 (0.67 0 0.11 0.11 0.11) *
##                       923) age< 92.5 293   174 B2 (0.33 0.41 0.16 0.1 0)  
##                        1846) stroke>=0.5 39    19 B1 (0.51 0.31 0.1 0.077 0) *
##                        1847) stroke< 0.5 254   147 B2 (0.3 0.42 0.17 0.11 0) *
##                   231) copd>=0.5 444   272 B2 (0.27 0.39 0.24 0.086 0.02)  
##                     462) osteoporosis< 0.5 282   187 B2 (0.31 0.34 0.25 0.082 0.018)  
##                       924) reimbursement2008< 27390 220   143 B1 (0.35 0.3 0.26 0.073 0.018)  
##                        1848) reimbursement2008>=12810 132    78 B1 (0.41 0.32 0.2 0.068 0.0076)  
##                          3696) age< 84.5 105    55 B1 (0.48 0.33 0.15 0.029 0.0095) *
##                          3697) age>=84.5 27    17 B3 (0.15 0.26 0.37 0.22 0) *
##                        1849) reimbursement2008< 12810 88    57 B3 (0.26 0.27 0.35 0.08 0.034) *
##                       925) reimbursement2008>=27390 62    33 B2 (0.18 0.47 0.23 0.11 0.016) *
##                     463) osteoporosis>=0.5 162    85 B2 (0.19 0.48 0.22 0.093 0.025) *
##              29) cancer>=0.5 2594  1539 B2 (0.26 0.41 0.24 0.091 0.01)  
##                58) reimbursement2008< 5770 1000   562 B2 (0.3 0.44 0.19 0.07 0.005) *
##                59) reimbursement2008>=5770 1594   977 B2 (0.23 0.39 0.27 0.1 0.014)  
##                 118) reimbursement2008>=8645 1054   656 B2 (0.27 0.38 0.24 0.1 0.015)  
##                   236) arthritis< 0.5 745   464 B2 (0.31 0.38 0.2 0.097 0.013)  
##                     472) ihd< 0.5 159    94 B1 (0.41 0.32 0.21 0.05 0.013)  
##                       944) reimbursement2008>=11995 76    36 B1 (0.53 0.24 0.16 0.066 0.013) *
##                       945) reimbursement2008< 11995 83    50 B2 (0.3 0.4 0.25 0.036 0.012) *
##                     473) ihd>=0.5 586   356 B2 (0.28 0.39 0.2 0.11 0.014) *
##                   237) arthritis>=0.5 309   192 B2 (0.16 0.38 0.32 0.12 0.019)  
##                     474) reimbursement2008>=10960 237   136 B2 (0.15 0.43 0.3 0.11 0.013)  
##                       948) copd< 0.5 126    64 B2 (0.17 0.49 0.26 0.071 0) *
##                       949) copd>=0.5 111    72 B2 (0.12 0.35 0.35 0.15 0.027)  
##                        1898) age< 75.5 54    30 B3 (0.15 0.26 0.44 0.13 0.019) *
##                        1899) age>=75.5 57    32 B2 (0.088 0.44 0.26 0.18 0.035) *
##                     475) reimbursement2008< 10960 72    44 B3 (0.19 0.22 0.39 0.15 0.042) *
##                 119) reimbursement2008< 8645 540   321 B2 (0.16 0.41 0.32 0.11 0.011)  
##                   238) heart.failure>=0.5 243   128 B2 (0.14 0.47 0.28 0.099 0.016) *
##                   239) heart.failure< 0.5 297   191 B3 (0.18 0.35 0.36 0.11 0.0067)  
##                     478) depression< 0.5 226   141 B2 (0.18 0.38 0.33 0.12 0.0044) *
##                     479) depression>=0.5 71    39 B3 (0.17 0.27 0.45 0.099 0.014) *
##            15) diabetes>=0.5 53801 31450 B2 (0.23 0.42 0.21 0.13 0.02)  
##              30) kidney< 0.5 25067 14311 B2 (0.3 0.43 0.19 0.076 0.0074)  
##                60) arthritis< 0.5 15178  9179 B2 (0.35 0.4 0.17 0.069 0.0063)  
##                 120) cancer< 0.5 12572  7709 B2 (0.39 0.39 0.16 0.063 0.0059)  
##                   240) ihd< 0.5 2617  1376 B1 (0.47 0.34 0.13 0.049 0.0053)  
##                     480) reimbursement2008>=9400 403   171 B1 (0.58 0.21 0.15 0.05 0.0099) *
##                     481) reimbursement2008< 9400 2214  1205 B1 (0.46 0.36 0.13 0.048 0.0045)  
##                       962) osteoporosis< 0.5 1636   847 B1 (0.48 0.34 0.12 0.049 0.0043)  
##                        1924) alzheimers< 0.5 1127   559 B1 (0.5 0.33 0.12 0.042 0.0035) *
##                        1925) alzheimers>=0.5 509   288 B1 (0.43 0.36 0.13 0.065 0.0059)  
##                          3850) reimbursement2008< 3775 137    68 B1 (0.5 0.3 0.12 0.066 0.0073) *
##                          3851) reimbursement2008>=3775 372   220 B1 (0.41 0.39 0.13 0.065 0.0054)  
##                            7702) reimbursement2008>=4055 330   188 B1 (0.43 0.36 0.13 0.07 0.0061)  
##                             15404) reimbursement2008>=4185 309   177 B1 (0.43 0.38 0.12 0.065 0.0065)  
##                               30808) reimbursement2008>=4635 253   138 B1 (0.45 0.35 0.12 0.067 0.0079)  
##                                 61616) age< 96 245   131 B1 (0.47 0.36 0.11 0.065 0.0041)  
##                                  123232) reimbursement2008< 8170 209   106 B1 (0.49 0.33 0.11 0.067 0) *
##                                  123233) reimbursement2008>=8170 36    19 B2 (0.31 0.47 0.14 0.056 0.028)  
##                                    246466) age< 74.5 15     6 B1 (0.6 0.13 0.2 0 0.067) *
##                                    246467) age>=74.5 21     6 B2 (0.095 0.71 0.095 0.095 0) *
##                                 61617) age>=96 8     5 B3 (0.12 0.25 0.38 0.12 0.12) *
##                               30809) reimbursement2008< 4635 56    28 B2 (0.3 0.5 0.14 0.054 0) *
##                             15405) reimbursement2008< 4185 21    11 B1 (0.48 0.095 0.29 0.14 0) *
##                            7703) reimbursement2008< 4055 42    17 B2 (0.24 0.6 0.14 0.024 0) *
##                       963) osteoporosis>=0.5 578   342 B2 (0.38 0.41 0.16 0.047 0.0052)  
##                        1926) depression< 0.5 339   189 B1 (0.44 0.37 0.13 0.047 0.0029)  
##                          3852) reimbursement2008< 4905 211   119 B1 (0.44 0.42 0.11 0.033 0)  
##                            7704) reimbursement2008< 4075 142    71 B1 (0.5 0.36 0.11 0.035 0) *
##                            7705) reimbursement2008>=4075 69    31 B2 (0.3 0.55 0.12 0.029 0) *
##                          3853) reimbursement2008>=4905 128    70 B1 (0.45 0.3 0.17 0.07 0.0078) *
##                        1927) depression>=0.5 239   130 B2 (0.29 0.46 0.2 0.046 0.0084)  
##                          3854) copd< 0.5 181    88 B2 (0.31 0.51 0.13 0.039 0.011) *
##                          3855) copd>=0.5 58    34 B3 (0.24 0.28 0.41 0.069 0) *
##                   241) ihd>=0.5 9955  5976 B2 (0.36 0.4 0.17 0.067 0.006)  
##                     482) depression< 0.5 5563  3339 B1 (0.4 0.38 0.15 0.059 0.0059)  
##                       964) reimbursement2008>=8955 1363   758 B1 (0.44 0.32 0.16 0.067 0.0088)  
##                        1928) copd< 0.5 798   405 B1 (0.49 0.32 0.12 0.064 0.0038) *
##                        1929) copd>=0.5 565   353 B1 (0.38 0.32 0.22 0.073 0.016)  
##                          3858) stroke>=0.5 116    64 B2 (0.35 0.45 0.12 0.06 0.017)  
##                            7716) age>=74.5 63    36 B1 (0.43 0.33 0.16 0.063 0.016) *
##                            7717) age< 74.5 53    22 B2 (0.26 0.58 0.075 0.057 0.019) *
##                          3859) stroke< 0.5 449   278 B1 (0.38 0.28 0.24 0.076 0.016) *
##                       965) reimbursement2008< 8955 4200  2510 B2 (0.39 0.4 0.15 0.056 0.005)  
##                        1930) heart.failure< 0.5 1953  1129 B1 (0.42 0.4 0.13 0.045 0.0041)  
##                          3860) reimbursement2008< 3415 343   172 B1 (0.5 0.37 0.096 0.032 0.0058) *
##                          3861) reimbursement2008>=3415 1610   954 B2 (0.41 0.41 0.14 0.048 0.0037)  
##                            7722) age< 42.5 43    17 B1 (0.6 0.26 0.07 0.07 0) *
##                            7723) age>=42.5 1567   922 B2 (0.4 0.41 0.14 0.047 0.0038)  
##                             15446) age>=50.5 1527   894 B2 (0.4 0.41 0.13 0.047 0.0039)  
##                               30892) reimbursement2008>=3465 1478   870 B2 (0.41 0.41 0.13 0.045 0.0027)  
##                                 61784) reimbursement2008< 4655 759   431 B1 (0.43 0.4 0.12 0.043 0.0013)  
##                                  123568) reimbursement2008>=4315 158    79 B1 (0.5 0.35 0.095 0.051 0.0063) *
##                                  123569) reimbursement2008< 4315 601   352 B1 (0.41 0.41 0.13 0.042 0)  
##                                    247138) reimbursement2008< 4295 592   344 B1 (0.42 0.41 0.13 0.042 0)  
##                                      494276) age>=82.5 135    71 B1 (0.47 0.36 0.15 0.015 0) *
##                                      494277) age< 82.5 457   266 B2 (0.4 0.42 0.13 0.05 0)  
##                                        988554) age< 74.5 290   166 B1 (0.43 0.39 0.13 0.052 0)  
##                                         1977108) age>=62.5 234   128 B1 (0.45 0.38 0.12 0.047 0) *
##                                         1977109) age< 62.5 56    31 B2 (0.32 0.45 0.16 0.071 0) *
##                                        988555) age>=74.5 167    90 B2 (0.36 0.46 0.13 0.048 0)  
##                                         1977110) reimbursement2008>=4105 39    20 B1 (0.49 0.36 0.13 0.026 0) *
##                                         1977111) reimbursement2008< 4105 128    65 B2 (0.32 0.49 0.13 0.055 0) *
##                                    247139) reimbursement2008>=4295 9     1 B2 (0.11 0.89 0 0 0) *
##                                 61785) reimbursement2008>=4655 719   414 B2 (0.38 0.42 0.15 0.047 0.0042)  
##                                  123570) reimbursement2008< 5835 346   180 B2 (0.35 0.48 0.13 0.038 0.0029) *
##                                  123571) reimbursement2008>=5835 373   223 B1 (0.4 0.37 0.16 0.056 0.0054)  
##                                    247142) alzheimers>=0.5 124    64 B1 (0.48 0.31 0.15 0.04 0.0081)  
##                                      494284) reimbursement2008< 8555 114    55 B1 (0.52 0.28 0.16 0.035 0.0088) *
##                                      494285) reimbursement2008>=8555 10     3 B2 (0.1 0.7 0.1 0.1 0) *
##                                    247143) alzheimers< 0.5 249   149 B2 (0.36 0.4 0.17 0.064 0.004)  
##                                      494286) reimbursement2008>=6045 217   124 B2 (0.36 0.43 0.14 0.069 0.0046) *
##                                      494287) reimbursement2008< 6045 32    20 B1 (0.38 0.22 0.38 0.031 0)  
##                                        988574) age< 72.5 11     4 B1 (0.64 0.18 0.18 0 0) *
##                                        988575) age>=72.5 21    11 B3 (0.24 0.24 0.48 0.048 0) *
##                               30893) reimbursement2008< 3465 49    24 B2 (0.27 0.51 0.082 0.1 0.041) *
##                             15447) age< 50.5 40    26 B1 (0.35 0.3 0.3 0.05 0) *
##                        1931) heart.failure>=0.5 2247  1339 B2 (0.35 0.4 0.17 0.066 0.0058)  
##                          3862) reimbursement2008>=5335 866   530 B1 (0.39 0.37 0.16 0.074 0.0058)  
##                            7724) reimbursement2008>=8115 129    68 B2 (0.36 0.47 0.12 0.047 0) *
##                            7725) reimbursement2008< 8115 737   447 B1 (0.39 0.35 0.17 0.079 0.0068)  
##                             15450) age< 94.5 703   421 B1 (0.4 0.35 0.17 0.075 0.0071)  
##                               30900) reimbursement2008>=6635 298   164 B1 (0.45 0.32 0.15 0.067 0.013) *
##                               30901) reimbursement2008< 6635 405   255 B2 (0.37 0.37 0.18 0.081 0.0025)  
##                                 61802) reimbursement2008< 5685 137    80 B1 (0.42 0.31 0.16 0.11 0) *
##                                 61803) reimbursement2008>=5685 268   161 B2 (0.34 0.4 0.19 0.067 0.0037) *
##                             15451) age>=94.5 34    17 B2 (0.24 0.5 0.12 0.15 0) *
##                          3863) reimbursement2008< 5335 1381   795 B2 (0.33 0.42 0.18 0.061 0.0058)  
##                            7726) copd< 0.5 997   591 B2 (0.36 0.41 0.17 0.057 0.006)  
##                             15452) age< 69.5 297   171 B1 (0.42 0.38 0.15 0.04 0.0034)  
##                               30904) reimbursement2008< 5065 274   153 B1 (0.44 0.36 0.15 0.04 0.0036)  
##                                 61808) alzheimers< 0.5 174    91 B1 (0.48 0.3 0.17 0.046 0.0057) *
##                                 61809) alzheimers>=0.5 100    54 B2 (0.38 0.46 0.13 0.03 0)  
##                                  123618) reimbursement2008>=4355 26    10 B1 (0.62 0.15 0.15 0.077 0) *
##                                  123619) reimbursement2008< 4355 74    32 B2 (0.3 0.57 0.12 0.014 0) *
##                               30905) reimbursement2008>=5065 23     9 B2 (0.22 0.61 0.13 0.043 0) *
##                             15453) age>=69.5 700   407 B2 (0.33 0.42 0.18 0.064 0.0071) *
##                            7727) copd>=0.5 384   204 B2 (0.27 0.47 0.19 0.07 0.0052) *
##                     483) depression>=0.5 4392  2538 B2 (0.31 0.42 0.18 0.077 0.0061)  
##                       966) reimbursement2008< 8325 2928  1619 B2 (0.31 0.45 0.17 0.065 0.0065)  
##                        1932) copd< 0.5 1987  1102 B2 (0.33 0.45 0.16 0.056 0.0045)  
##                          3864) age< 98.5 1964  1085 B2 (0.33 0.45 0.16 0.057 0.0046)  
##                            7728) reimbursement2008< 3085 22     8 B1 (0.64 0.23 0.045 0.091 0) *
##                            7729) reimbursement2008>=3085 1942  1068 B2 (0.33 0.45 0.16 0.056 0.0046)  
##                             15458) heart.failure< 0.5 889   508 B2 (0.37 0.43 0.15 0.051 0.0034) *
##                             15459) heart.failure>=0.5 1053   560 B2 (0.3 0.47 0.17 0.061 0.0057)  
##                               30918) osteoporosis< 0.5 721   396 B2 (0.32 0.45 0.16 0.061 0.0055)  
##                                 61836) age>=86.5 109    59 B1 (0.46 0.3 0.15 0.083 0.0092) *
##                                 61837) age< 86.5 612   320 B2 (0.3 0.48 0.16 0.057 0.0049) *
##                               30919) osteoporosis>=0.5 332   164 B2 (0.24 0.51 0.18 0.06 0.006) *
##                          3865) age>=98.5 23    12 B3 (0.26 0.26 0.48 0 0) *
##                        1933) copd>=0.5 941   517 B2 (0.26 0.45 0.2 0.084 0.011) *
##                       967) reimbursement2008>=8325 1464   919 B2 (0.32 0.37 0.2 0.1 0.0055)  
##                        1934) reimbursement2008< 8485 36    16 B1 (0.56 0.22 0.22 0 0) *
##                        1935) reimbursement2008>=8485 1428   891 B2 (0.32 0.38 0.2 0.1 0.0056)  
##                          3870) age< 78.5 837   532 B2 (0.35 0.36 0.19 0.098 0.0036)  
##                            7740) reimbursement2008< 21320 639   406 B1 (0.36 0.35 0.19 0.092 0.0031)  
##                             15480) age< 49.5 83    47 B2 (0.35 0.43 0.096 0.12 0) *
##                             15481) age>=49.5 556   352 B1 (0.37 0.33 0.21 0.088 0.0036)  
##                               30962) age>=67.5 368   230 B1 (0.38 0.36 0.18 0.087 0)  
##                                 61924) reimbursement2008>=10440 261   153 B1 (0.41 0.33 0.17 0.084 0)  
##                                  123848) reimbursement2008< 12585 92    46 B1 (0.5 0.25 0.18 0.065 0) *
##                                  123849) reimbursement2008>=12585 169   105 B2 (0.37 0.38 0.16 0.095 0)  
##                                    247698) reimbursement2008>=14485 109    63 B1 (0.42 0.31 0.17 0.092 0)  
##                                      495396) osteoporosis< 0.5 72    37 B1 (0.49 0.24 0.17 0.11 0) *
##                                      495397) osteoporosis>=0.5 37    20 B2 (0.3 0.46 0.19 0.054 0) *
##                                    247699) reimbursement2008< 14485 60    30 B2 (0.27 0.5 0.13 0.1 0) *
##                                 61925) reimbursement2008< 10440 107    62 B2 (0.28 0.42 0.21 0.093 0) *
##                               30963) age< 67.5 188   122 B1 (0.35 0.28 0.27 0.09 0.011)  
##                                 61926) age>=55.5 135    82 B1 (0.39 0.25 0.27 0.089 0) *
##                                 61927) age< 55.5 53    34 B2 (0.25 0.36 0.26 0.094 0.038) *
##                            7741) reimbursement2008>=21320 198   114 B2 (0.3 0.42 0.16 0.12 0.0051) *
##                          3871) age>=78.5 591   359 B2 (0.28 0.39 0.21 0.11 0.0085)  
##                            7742) heart.failure< 0.5 122    73 B1 (0.4 0.32 0.18 0.098 0)  
##                             15484) reimbursement2008< 11560 40    17 B1 (0.58 0.2 0.18 0.05 0) *
##                             15485) reimbursement2008>=11560 82    51 B2 (0.32 0.38 0.18 0.12 0) *
##                            7743) heart.failure>=0.5 469   276 B2 (0.24 0.41 0.22 0.11 0.011) *
##                 121) cancer>=0.5 2606  1470 B2 (0.21 0.44 0.25 0.098 0.0081) *
##                61) arthritis>=0.5 9889  5132 B2 (0.22 0.48 0.21 0.088 0.0092)  
##                 122) depression< 0.5 5134  2665 B2 (0.25 0.48 0.18 0.08 0.0078)  
##                   244) cancer< 0.5 4305  2260 B2 (0.27 0.48 0.17 0.076 0.0086)  
##                     488) reimbursement2008>=9880 1063   636 B2 (0.32 0.4 0.18 0.089 0.012)  
##                       976) ihd< 0.5 102    49 B1 (0.52 0.27 0.13 0.069 0.0098) *
##                       977) ihd>=0.5 961   562 B2 (0.29 0.42 0.19 0.092 0.012) *
##                     489) reimbursement2008< 9880 3242  1624 B2 (0.25 0.5 0.17 0.072 0.0074) *
##                   245) cancer>=0.5 829   405 B2 (0.15 0.51 0.23 0.1 0.0036) *
##                 123) depression>=0.5 4755  2467 B2 (0.18 0.48 0.23 0.096 0.011) *
##              31) kidney>=0.5 28734 17139 B2 (0.16 0.4 0.23 0.17 0.03)  
##                62) reimbursement2008< 15395 16249  9131 B2 (0.19 0.44 0.24 0.12 0.016)  
##                 124) arthritis< 0.5 9424  5647 B2 (0.23 0.4 0.23 0.12 0.017)  
##                   248) cancer< 0.5 7786  4711 B2 (0.25 0.39 0.21 0.12 0.017)  
##                     496) ihd< 0.5 964   608 B1 (0.37 0.36 0.17 0.085 0.011)  
##                       992) depression< 0.5 572   338 B1 (0.41 0.32 0.16 0.1 0.01)  
##                        1984) reimbursement2008< 3545 101    53 B2 (0.33 0.48 0.15 0.04 0.0099) *
##                        1985) reimbursement2008>=3545 471   270 B1 (0.43 0.29 0.16 0.11 0.011)  
##                          3970) osteoporosis< 0.5 346   186 B1 (0.46 0.27 0.15 0.11 0.014) *
##                          3971) osteoporosis>=0.5 125    82 B2 (0.33 0.34 0.2 0.13 0)  
##                            7942) age>=62 106    67 B1 (0.37 0.37 0.15 0.11 0)  
##                             15884) age>=67.5 93    55 B2 (0.34 0.41 0.16 0.086 0)  
##                               31768) reimbursement2008>=6110 44    23 B1 (0.48 0.3 0.11 0.11 0)  
##                                 63536) reimbursement2008< 9180 26     9 B1 (0.65 0.15 0.077 0.12 0) *
##                                 63537) reimbursement2008>=9180 18     9 B2 (0.22 0.5 0.17 0.11 0) *
##                               31769) reimbursement2008< 6110 49    24 B2 (0.22 0.51 0.2 0.061 0) *
##                             15885) age< 67.5 13     6 B1 (0.54 0.077 0.077 0.31 0) *
##                            7943) age< 62 19    10 B3 (0.11 0.21 0.47 0.21 0) *
##                       993) depression>=0.5 392   227 B2 (0.31 0.42 0.19 0.064 0.013)  
##                        1986) reimbursement2008>=14460 9     2 B1 (0.78 0.22 0 0 0) *
##                        1987) reimbursement2008< 14460 383   220 B2 (0.3 0.43 0.2 0.065 0.013) *
##                     497) ihd>=0.5 6822  4095 B2 (0.24 0.4 0.22 0.12 0.018)  
##                       994) reimbursement2008< 6325 3172  1786 B2 (0.22 0.44 0.22 0.11 0.016) *
##                       995) reimbursement2008>=6325 3650  2309 B2 (0.25 0.37 0.22 0.14 0.019)  
##                        1990) osteoporosis< 0.5 2424  1594 B2 (0.27 0.34 0.23 0.14 0.02)  
##                          3980) depression< 0.5 1234   816 B2 (0.3 0.34 0.2 0.13 0.024)  
##                            7960) reimbursement2008>=12135 349   226 B1 (0.35 0.3 0.16 0.16 0.032)  
##                             15920) age>=54 331   210 B1 (0.37 0.28 0.16 0.16 0.03) *
##                             15921) age< 54 18     9 B2 (0.11 0.5 0.11 0.22 0.056) *
##                            7961) reimbursement2008< 12135 885   570 B2 (0.28 0.36 0.22 0.12 0.021) *
##                          3981) depression>=0.5 1190   778 B2 (0.24 0.35 0.25 0.15 0.016)  
##                            7962) copd< 0.5 547   367 B2 (0.28 0.33 0.25 0.12 0.022)  
##                             15924) reimbursement2008>=9205 310   209 B1 (0.33 0.32 0.21 0.12 0.029)  
##                               31848) reimbursement2008< 9955 50    28 B2 (0.42 0.44 0.02 0.12 0) *
##                               31849) reimbursement2008>=9955 260   180 B1 (0.31 0.3 0.24 0.12 0.035)  
##                                 63698) reimbursement2008>=14765 20     9 B1 (0.55 0.25 0.1 0.05 0.05) *
##                                 63699) reimbursement2008< 14765 240   168 B2 (0.29 0.3 0.25 0.12 0.033)  
##                                  127398) age>=61.5 201   138 B1 (0.31 0.29 0.24 0.13 0.02)  
##                                    254796) reimbursement2008< 12625 112    69 B1 (0.38 0.24 0.28 0.089 0.0089) *
##                                    254797) reimbursement2008>=12625 89    58 B2 (0.22 0.35 0.2 0.19 0.034) *
##                                  127399) age< 61.5 39    25 B2 (0.15 0.36 0.31 0.077 0.1) *
##                             15925) reimbursement2008< 9205 237   156 B2 (0.22 0.34 0.3 0.12 0.013)  
##                               31850) age< 67.5 56    29 B2 (0.2 0.48 0.16 0.16 0) *
##                               31851) age>=67.5 181   118 B3 (0.23 0.3 0.35 0.1 0.017)  
##                                 63702) reimbursement2008>=6865 136    82 B3 (0.25 0.26 0.4 0.074 0.015) *
##                                 63703) reimbursement2008< 6865 45    27 B2 (0.18 0.4 0.2 0.2 0.022) *
##                            7963) copd>=0.5 643   411 B2 (0.21 0.36 0.25 0.17 0.011) *
##                        1991) osteoporosis>=0.5 1226   715 B2 (0.21 0.42 0.22 0.14 0.017) *
##                   249) cancer>=0.5 1638   936 B2 (0.13 0.43 0.29 0.14 0.016) *
##                 125) arthritis>=0.5 6825  3484 B2 (0.13 0.49 0.25 0.12 0.014) *
##                63) reimbursement2008>=15395 12485  8008 B2 (0.13 0.36 0.23 0.24 0.049)  
##                 126) arthritis>=0.5 5402  3220 B2 (0.094 0.4 0.24 0.22 0.04)  
##                   252) reimbursement2008< 34925 3345  1942 B2 (0.11 0.42 0.25 0.19 0.03)  
##                     504) depression< 0.5 1291   714 B2 (0.14 0.45 0.22 0.17 0.025)  
##                      1008) cancer< 0.5 973   546 B2 (0.16 0.44 0.19 0.18 0.029) *
##                      1009) cancer>=0.5 318   168 B2 (0.072 0.47 0.3 0.14 0.013)  
##                        2018) reimbursement2008>=16525 293   144 B2 (0.068 0.51 0.28 0.13 0.014) *
##                        2019) reimbursement2008< 16525 25    10 B3 (0.12 0.04 0.6 0.24 0) *
##                     505) depression>=0.5 2054  1228 B2 (0.092 0.4 0.27 0.2 0.034) *
##                   253) reimbursement2008>=34925 2057  1278 B2 (0.067 0.38 0.23 0.27 0.055)  
##                     506) copd< 0.5 520   300 B2 (0.096 0.42 0.23 0.21 0.042) *
##                     507) copd>=0.5 1537   978 B2 (0.057 0.36 0.23 0.29 0.06)  
##                      1014) age>=62.5 1286   804 B2 (0.058 0.37 0.24 0.27 0.061) *
##                      1015) age< 62.5 251   153 B4 (0.052 0.31 0.2 0.39 0.052)  
##                        2030) reimbursement2008< 101585 237   150 B4 (0.055 0.32 0.21 0.37 0.051)  
##                          4060) cancer>=0.5 62    36 B2 (0.048 0.42 0.27 0.24 0.016) *
##                          4061) cancer< 0.5 175   103 B4 (0.057 0.29 0.18 0.41 0.063) *
##                        2031) reimbursement2008>=101585 14     3 B4 (0 0.071 0.071 0.79 0.071) *
##                 127) arthritis< 0.5 7083  4788 B2 (0.15 0.32 0.22 0.25 0.057)  
##                   254) cancer< 0.5 5298  3651 B2 (0.17 0.31 0.2 0.26 0.062)  
##                     508) depression< 0.5 2489  1797 B2 (0.22 0.28 0.18 0.27 0.06)  
##                      1016) copd>=0.5 1317   890 B2 (0.2 0.32 0.18 0.24 0.056)  
##                        2032) ihd< 0.5 72    41 B1 (0.43 0.25 0.15 0.11 0.056) *
##                        2033) ihd>=0.5 1245   836 B2 (0.19 0.33 0.18 0.24 0.056) *
##                      1017) copd< 0.5 1172   815 B4 (0.23 0.23 0.17 0.3 0.065)  
##                        2034) reimbursement2008>=43640 191   129 B2 (0.15 0.32 0.23 0.22 0.073)  
##                          4068) age>=64.5 172   112 B2 (0.16 0.35 0.2 0.23 0.064) *
##                          4069) age< 64.5 19    10 B3 (0.11 0.11 0.47 0.16 0.16) *
##                        2035) reimbursement2008< 43640 981   666 B4 (0.25 0.21 0.16 0.32 0.063)  
##                          4070) reimbursement2008< 23175 468   337 B1 (0.28 0.24 0.16 0.26 0.056)  
##                            8140) age< 93.5 457   326 B1 (0.29 0.23 0.16 0.26 0.057)  
##                             16280) age< 86.5 398   288 B1 (0.28 0.25 0.16 0.25 0.063)  
##                               32560) alzheimers>=0.5 179   122 B1 (0.32 0.28 0.15 0.17 0.073)  
##                                 65120) reimbursement2008>=21440 38    19 B2 (0.24 0.5 0.11 0.13 0.026) *
##                                 65121) reimbursement2008< 21440 141    93 B1 (0.34 0.23 0.16 0.18 0.085)  
##                                  130242) reimbursement2008>=17585 89    53 B1 (0.4 0.17 0.19 0.16 0.079) *
##                                  130243) reimbursement2008< 17585 52    35 B2 (0.23 0.33 0.12 0.23 0.096) *
##                               32561) alzheimers< 0.5 219   151 B4 (0.24 0.23 0.16 0.31 0.055) *
##                             16281) age>=86.5 59    38 B1 (0.36 0.1 0.17 0.36 0.017)  
##                               32562) reimbursement2008>=19680 18     8 B1 (0.56 0.11 0 0.28 0.056) *
##                               32563) reimbursement2008< 19680 41    25 B4 (0.27 0.098 0.24 0.39 0) *
##                            8141) age>=93.5 11     6 B2 (0 0.45 0.36 0.18 0) *
##                          4071) reimbursement2008>=23175 513   320 B4 (0.22 0.18 0.16 0.38 0.07) *
##                     509) depression>=0.5 2809  1854 B2 (0.13 0.34 0.22 0.25 0.063) *
##                   255) cancer>=0.5 1785  1137 B2 (0.097 0.36 0.28 0.22 0.041) *
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 171981  12159    140    186      0
##        B2  26534  25373    156    196      0
##        B3  12021  12119    286    160      0
##        B4   4652   6824     70    360      0
##        B5    483   1029     14     60      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.7205162             NA      0.7188342      0.7221934      0.6712663 
## AccuracyPValue  McnemarPValue 
##      0.0000000      0.0000000
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow

##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 114141   8610    124    103      0
##        B2  18409  16102    187    142      0
##        B3   8027   8146    118     99      0
##        B4   3099   4584     53    201      0
##        B5    351    657      4     45      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.126669e-01             NA   7.105887e-01   7.147384e-01   6.712700e-01 
## AccuracyPValue  McnemarPValue 
##  7.015238e-319   0.000000e+00 
##                      model_id model_method
## 1 All.X.lser.no.cp.4015.rpart        rpart
##                                                                                                                                             feats
## 1 age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1               1                     73.967                 15.63
##   max.Accuracy.fit max.AccuracyLower.fit max.AccuracyUpper.fit
## 1        0.7117062             0.7188342             0.7221934
##   max.Kappa.fit max.Accuracy.OOB max.AccuracyLower.OOB
## 1     0.3328722        0.7126669             0.7105887
##   max.AccuracyUpper.OOB max.Kappa.OOB min.SSE.fit max.AccuracySD.fit
## 1             0.7147384            NA           0        0.002149621
##   max.KappaSD.fit
## 1      0.00733801
## [1] "fitting model: All.X.lser.ys.cp.opt.rpart"
## [1] "    indep_vars: age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008"
## + Fold1: cp=0.00435 
## - Fold1: cp=0.00435 
## + Fold2: cp=0.00435 
## - Fold2: cp=0.00435 
## + Fold3: cp=0.00435 
## - Fold3: cp=0.00435 
## + Fold4: cp=0.00435 
## - Fold4: cp=0.00435 
## + Fold5: cp=0.00435 
## - Fold5: cp=0.00435 
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.014 on full training set

## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7, 
##     cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, 
##     surrogatestyle = 0, maxdepth = 30, xval = 0))
##   n= 274803 
## 
##           CP nsplit rel error
## 1 0.04908841      0 1.0000000
## 2 0.01395884      2 0.9018232
## 
## Variable importance
## reimbursement2008        bucket2008               ihd          diabetes 
##                31                21                14                13 
##     heart.failure            kidney 
##                11                 9 
## 
## Node number 1: 274803 observations,    complexity param=0.04908841
##   predicted class=B1  expected loss=0.3287337  P(node) =1
##     class counts: 184466 52259 24586 11906  1586
##    probabilities: 0.671 0.190 0.089 0.043 0.006 
##   left son=2 (165987 obs) right son=3 (108816 obs)
##   Primary splits:
##       reimbursement2008 < 1565 to the left,  improve=24395.14, (0 missing)
##       bucket2008        < 1.5  to the left,  improve=20624.70, (0 missing)
##       ihd               < 0.5  to the left,  improve=16291.74, (0 missing)
##       diabetes          < 0.5  to the left,  improve=16041.26, (0 missing)
##       heart.failure     < 0.5  to the left,  improve=12498.16, (0 missing)
##   Surrogate splits:
##       bucket2008    < 1.5  to the left,  agree=0.861, adj=0.650, (0 split)
##       ihd           < 0.5  to the left,  agree=0.792, adj=0.474, (0 split)
##       diabetes      < 0.5  to the left,  agree=0.785, adj=0.456, (0 split)
##       heart.failure < 0.5  to the left,  agree=0.762, adj=0.399, (0 split)
##       kidney        < 0.5  to the left,  agree=0.731, adj=0.321, (0 split)
## 
## Node number 2: 165987 observations
##   predicted class=B1  expected loss=0.1261424  P(node) =0.6040218
##     class counts: 145049 12284  6102  2315   237
##    probabilities: 0.874 0.074 0.037 0.014 0.001 
## 
## Node number 3: 108816 observations,    complexity param=0.04908841
##   predicted class=B2  expected loss=0.6326367  P(node) =0.3959782
##     class counts: 39417 39975 18484  9591  1349
##    probabilities: 0.362 0.367 0.170 0.088 0.012 
##   left son=6 (39298 obs) right son=7 (69518 obs)
##   Primary splits:
##       reimbursement2008 < 3065 to the left,  improve=2010.3080, (0 missing)
##       bucket2008        < 1.5  to the left,  improve=1980.9770, (0 missing)
##       kidney            < 0.5  to the left,  improve=1416.9220, (0 missing)
##       diabetes          < 0.5  to the left,  improve=1236.1460, (0 missing)
##       heart.failure     < 0.5  to the left,  improve= 976.9427, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5  to the left,  agree=0.989, adj=0.969, (0 split)
##       ihd        < 0.5  to the left,  agree=0.659, adj=0.056, (0 split)
##       diabetes   < 0.5  to the left,  agree=0.641, adj=0.006, (0 split)
## 
## Node number 6: 39298 observations
##   predicted class=B1  expected loss=0.4797445  P(node) =0.1430043
##     class counts: 20445 12134  4756  1782   181
##    probabilities: 0.520 0.309 0.121 0.045 0.005 
## 
## Node number 7: 69518 observations
##   predicted class=B2  expected loss=0.5995138  P(node) =0.2529739
##     class counts: 18972 27841 13728  7809  1168
##    probabilities: 0.273 0.400 0.197 0.112 0.017 
## 
## n= 274803 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 274803 90337 B1 (0.67 0.19 0.089 0.043 0.0058)  
##   2) reimbursement2008< 1565 165987 20938 B1 (0.87 0.074 0.037 0.014 0.0014) *
##   3) reimbursement2008>=1565 108816 68841 B2 (0.36 0.37 0.17 0.088 0.012)  
##     6) reimbursement2008< 3065 39298 18853 B1 (0.52 0.31 0.12 0.045 0.0046) *
##     7) reimbursement2008>=3065 69518 41677 B2 (0.27 0.4 0.2 0.11 0.017) *
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 165494  18972      0      0      0
##        B2  24418  27841      0      0      0
##        B3  10858  13728      0      0      0
##        B4   4097   7809      0      0      0
##        B5    418   1168      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.035404e-01             NA   7.018289e-01   7.052475e-01   6.712663e-01 
## AccuracyPValue  McnemarPValue 
##  2.207353e-289            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 110452  12526      0      0      0
##        B2  16322  18518      0      0      0
##        B3   7105   9285      0      0      0
##        B4   2740   5197      0      0      0
##        B5    299    758      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.039770e-01             NA   7.018807e-01   7.060669e-01   6.712700e-01 
## AccuracyPValue  McnemarPValue 
##  6.148674e-199            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow

##                     model_id model_method
## 1 All.X.lser.ys.cp.opt.rpart        rpart
##                                                                                                                                             feats
## 1 age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1               3                    108.644                15.505
##   max.Accuracy.fit max.AccuracyLower.fit max.AccuracyUpper.fit
## 1        0.7035404             0.7018289             0.7052475
##   max.Kappa.fit min.loss.error.fit max.Accuracy.OOB max.AccuracyLower.OOB
## 1            NA          0.7784778         0.703977             0.7018807
##   max.AccuracyUpper.OOB max.Kappa.OOB min.loss.error.OOB min.SSE.fit
## 1             0.7060669            NA          0.7441403           0
##   min.loss.errorSD.fit
## 1           0.01642528
## [1] "fitting model: All.X.lser.ys.cp.4015.rpart"
## [1] "    indep_vars: age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008"
## + Fold1: cp=5e-05 
## - Fold1: cp=5e-05 
## + Fold2: cp=5e-05 
## - Fold2: cp=5e-05 
## + Fold3: cp=5e-05 
## - Fold3: cp=5e-05 
## + Fold4: cp=5e-05 
## - Fold4: cp=5e-05 
## + Fold5: cp=5e-05 
## - Fold5: cp=5e-05 
## Aggregating results
## Fitting final model on full training set
## Warning: labs do not fit even at cex 0.15, there may be some overplotting
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7, 
##     cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, 
##     surrogatestyle = 0, maxdepth = 30, xval = 0))
##   n= 274803 
## 
##              CP nsplit rel error
## 1  4.908841e-02      0 1.0000000
## 2  1.395884e-02      2 0.9018232
## 3  4.350377e-03      3 0.8878643
## 4  3.597640e-03      4 0.8835140
## 5  9.879673e-04      5 0.8799163
## 6  7.859460e-04     10 0.8748685
## 7  6.789392e-04     11 0.8740826
## 8  4.649258e-04     14 0.8720458
## 9  4.372516e-04     15 0.8715809
## 10 2.988809e-04     19 0.8698319
## 11 2.767415e-04     20 0.8695330
## 12 2.435326e-04     21 0.8692562
## 13 2.036818e-04     22 0.8690127
## 14 1.881842e-04     28 0.8677729
## 15 1.826494e-04     29 0.8675847
## 16 1.605101e-04     31 0.8672194
## 17 1.439056e-04     33 0.8668984
## 18 1.411382e-04     37 0.8663228
## 19 1.217663e-04     42 0.8655922
## 20 1.162314e-04     45 0.8652269
## 21 1.129105e-04     47 0.8649944
## 22 1.051618e-04     52 0.8644299
## 23 9.962695e-05     57 0.8638764
## 24 9.409212e-05     68 0.8627473
## 25 8.855729e-05     74 0.8621827
## 26 8.302246e-05     83 0.8613746
## 27 7.748763e-05     85 0.8612086
## 28 7.379774e-05     97 0.8602455
## 29 7.195280e-05    101 0.8599134
## 30 6.918538e-05    114 0.8588729
## 31 6.641797e-05    122 0.8583194
## 32 6.272808e-05    154 0.8560612
## 33 6.167383e-05    158 0.8557955
## 34 6.088314e-05    166 0.8552752
## 35 5.811572e-05    179 0.8544340
## 36 5.534831e-05    183 0.8542015
## 37 5.313437e-05    228 0.8516001
## 38 5.258089e-05    233 0.8513344
## 39 5.165842e-05    237 0.8511241
## 40 5.000000e-05    254 0.8501832
## 
## Variable importance
## reimbursement2008        bucket2008          diabetes               ihd 
##                31                20                13                13 
##     heart.failure            kidney         arthritis 
##                11                 9                 1 
## 
## Node number 1: 274803 observations,    complexity param=0.04908841
##   predicted class=B1  expected loss=0.3287337  P(node) =1
##     class counts: 184466 52259 24586 11906  1586
##    probabilities: 0.671 0.190 0.089 0.043 0.006 
##   left son=2 (165987 obs) right son=3 (108816 obs)
##   Primary splits:
##       reimbursement2008 < 1565   to the left,  improve=24395.14, (0 missing)
##       bucket2008        < 1.5    to the left,  improve=20624.70, (0 missing)
##       ihd               < 0.5    to the left,  improve=16291.74, (0 missing)
##       diabetes          < 0.5    to the left,  improve=16041.26, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=12498.16, (0 missing)
##   Surrogate splits:
##       bucket2008    < 1.5    to the left,  agree=0.861, adj=0.650, (0 split)
##       ihd           < 0.5    to the left,  agree=0.792, adj=0.474, (0 split)
##       diabetes      < 0.5    to the left,  agree=0.785, adj=0.456, (0 split)
##       heart.failure < 0.5    to the left,  agree=0.762, adj=0.399, (0 split)
##       kidney        < 0.5    to the left,  agree=0.731, adj=0.321, (0 split)
## 
## Node number 2: 165987 observations
##   predicted class=B1  expected loss=0.1261424  P(node) =0.6040218
##     class counts: 145049 12284  6102  2315   237
##    probabilities: 0.874 0.074 0.037 0.014 0.001 
## 
## Node number 3: 108816 observations,    complexity param=0.04908841
##   predicted class=B2  expected loss=0.6326367  P(node) =0.3959782
##     class counts: 39417 39975 18484  9591  1349
##    probabilities: 0.362 0.367 0.170 0.088 0.012 
##   left son=6 (39298 obs) right son=7 (69518 obs)
##   Primary splits:
##       reimbursement2008 < 3065   to the left,  improve=2010.3080, (0 missing)
##       bucket2008        < 1.5    to the left,  improve=1980.9770, (0 missing)
##       kidney            < 0.5    to the left,  improve=1416.9220, (0 missing)
##       diabetes          < 0.5    to the left,  improve=1236.1460, (0 missing)
##       heart.failure     < 0.5    to the left,  improve= 976.9427, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the left,  agree=0.989, adj=0.969, (0 split)
##       ihd        < 0.5    to the left,  agree=0.659, adj=0.056, (0 split)
##       diabetes   < 0.5    to the left,  agree=0.641, adj=0.006, (0 split)
## 
## Node number 6: 39298 observations,    complexity param=0.0006789392
##   predicted class=B1  expected loss=0.4797445  P(node) =0.1430043
##     class counts: 20445 12134  4756  1782   181
##    probabilities: 0.520 0.309 0.121 0.045 0.005 
##   left son=12 (20077 obs) right son=13 (19221 obs)
##   Primary splits:
##       reimbursement2008 < 2175   to the left,  improve=192.7592, (0 missing)
##       diabetes          < 0.5    to the left,  improve=155.3521, (0 missing)
##       ihd               < 0.5    to the left,  improve=114.8541, (0 missing)
##       arthritis         < 0.5    to the left,  improve=114.6837, (0 missing)
##       kidney            < 0.5    to the left,  improve=108.9096, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the left,  agree=0.542, adj=0.063, (0 split)
##       arthritis  < 0.5    to the left,  agree=0.539, adj=0.058, (0 split)
##       ihd        < 0.5    to the left,  agree=0.534, adj=0.048, (0 split)
##       kidney     < 0.5    to the left,  agree=0.532, adj=0.044, (0 split)
##       diabetes   < 0.5    to the left,  agree=0.532, adj=0.043, (0 split)
## 
## Node number 7: 69518 observations,    complexity param=0.01395884
##   predicted class=B2  expected loss=0.5995138  P(node) =0.2529739
##     class counts: 18972 27841 13728  7809  1168
##    probabilities: 0.273 0.400 0.197 0.112 0.017 
##   left son=14 (15717 obs) right son=15 (53801 obs)
##   Primary splits:
##       diabetes      < 0.5    to the left,  improve=646.4740, (0 missing)
##       kidney        < 0.5    to the left,  improve=604.0313, (0 missing)
##       arthritis     < 0.5    to the left,  improve=501.1263, (0 missing)
##       ihd           < 0.5    to the left,  improve=427.9009, (0 missing)
##       heart.failure < 0.5    to the left,  improve=380.0080, (0 missing)
## 
## Node number 12: 20077 observations,    complexity param=9.409212e-05
##   predicted class=B1  expected loss=0.4247148  P(node) =0.07305961
##     class counts: 11550  5416  2200   834    77
##    probabilities: 0.575 0.270 0.110 0.042 0.004 
##   left son=24 (8826 obs) right son=25 (11251 obs)
##   Primary splits:
##       diabetes      < 0.5    to the left,  improve=62.34344, (0 missing)
##       kidney        < 0.5    to the left,  improve=42.15624, (0 missing)
##       ihd           < 0.5    to the left,  improve=40.01287, (0 missing)
##       heart.failure < 0.5    to the left,  improve=36.00697, (0 missing)
##       arthritis     < 0.5    to the left,  improve=33.77686, (0 missing)
##   Surrogate splits:
##       ihd < 0.5    to the left,  agree=0.588, adj=0.062, (0 split)
## 
## Node number 13: 19221 observations,    complexity param=0.0006789392
##   predicted class=B1  expected loss=0.5372249  P(node) =0.06994465
##     class counts:  8895  6718  2556   948   104
##    probabilities: 0.463 0.350 0.133 0.049 0.005 
##   left son=26 (7137 obs) right son=27 (12084 obs)
##   Primary splits:
##       diabetes      < 0.5    to the left,  improve=71.31724, (0 missing)
##       arthritis     < 0.5    to the left,  improve=61.00585, (0 missing)
##       ihd           < 0.5    to the left,  improve=55.20411, (0 missing)
##       heart.failure < 0.5    to the left,  improve=52.20163, (0 missing)
##       kidney        < 0.5    to the left,  improve=49.73230, (0 missing)
## 
## Node number 14: 15717 observations,    complexity param=0.004350377
##   predicted class=B1  expected loss=0.5704651  P(node) =0.0571937
##     class counts:  6751  5490  2365   999   112
##    probabilities: 0.430 0.349 0.150 0.064 0.007 
##   left son=28 (13123 obs) right son=29 (2594 obs)
##   Primary splits:
##       cancer       < 0.5    to the left,  improve=130.12270, (0 missing)
##       arthritis    < 0.5    to the left,  improve=125.41530, (0 missing)
##       ihd          < 0.5    to the left,  improve= 80.76118, (0 missing)
##       depression   < 0.5    to the left,  improve= 61.32779, (0 missing)
##       osteoporosis < 0.5    to the left,  improve= 44.50253, (0 missing)
## 
## Node number 15: 53801 observations,    complexity param=0.0009879673
##   predicted class=B2  expected loss=0.5845616  P(node) =0.1957802
##     class counts: 12221 22351 11363  6810  1056
##    probabilities: 0.227 0.415 0.211 0.127 0.020 
##   left son=30 (25067 obs) right son=31 (28734 obs)
##   Primary splits:
##       kidney            < 0.5    to the left,  improve=408.9756, (0 missing)
##       reimbursement2008 < 15395  to the left,  improve=327.1281, (0 missing)
##       bucket2008        < 3.5    to the left,  improve=313.8191, (0 missing)
##       arthritis         < 0.5    to the left,  improve=266.5595, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=209.4718, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 8365   to the left,  agree=0.666, adj=0.282, (0 split)
##       bucket2008        < 2.5    to the left,  agree=0.664, adj=0.279, (0 split)
##       heart.failure     < 0.5    to the left,  agree=0.628, adj=0.201, (0 split)
##       copd              < 0.5    to the left,  agree=0.595, adj=0.132, (0 split)
##       ihd               < 0.5    to the left,  agree=0.575, adj=0.089, (0 split)
## 
## Node number 24: 8826 observations
##   predicted class=B1  expected loss=0.3716293  P(node) =0.03211755
##     class counts:  5546  2137   805   312    26
##    probabilities: 0.628 0.242 0.091 0.035 0.003 
## 
## Node number 25: 11251 observations,    complexity param=9.409212e-05
##   predicted class=B1  expected loss=0.4663585  P(node) =0.04094206
##     class counts:  6004  3279  1395   522    51
##    probabilities: 0.534 0.291 0.124 0.046 0.005 
##   left son=50 (9007 obs) right son=51 (2244 obs)
##   Primary splits:
##       kidney            < 0.5    to the left,  improve=18.86048, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=17.29926, (0 missing)
##       arthritis         < 0.5    to the left,  improve=16.91283, (0 missing)
##       reimbursement2008 < 1875   to the left,  improve=16.48954, (0 missing)
##       cancer            < 0.5    to the left,  improve=14.98495, (0 missing)
## 
## Node number 26: 7137 observations,    complexity param=0.0001051618
##   predicted class=B1  expected loss=0.470786  P(node) =0.02597133
##     class counts:  3777  2233   794   300    33
##    probabilities: 0.529 0.313 0.111 0.042 0.005 
##   left son=52 (5554 obs) right son=53 (1583 obs)
##   Primary splits:
##       arthritis  < 0.5    to the left,  improve=24.840370, (0 missing)
##       depression < 0.5    to the left,  improve=16.217060, (0 missing)
##       ihd        < 0.5    to the left,  improve=13.895180, (0 missing)
##       copd       < 0.5    to the left,  improve=12.688930, (0 missing)
##       kidney     < 0.5    to the left,  improve= 9.728645, (0 missing)
## 
## Node number 27: 12084 observations,    complexity param=0.0006789392
##   predicted class=B1  expected loss=0.5764647  P(node) =0.04397332
##     class counts:  5118  4485  1762   648    71
##    probabilities: 0.424 0.371 0.146 0.054 0.006 
##   left son=54 (8413 obs) right son=55 (3671 obs)
##   Primary splits:
##       arthritis         < 0.5    to the left,  improve=27.83165, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=26.70933, (0 missing)
##       ihd               < 0.5    to the left,  improve=24.37311, (0 missing)
##       kidney            < 0.5    to the left,  improve=22.60183, (0 missing)
##       reimbursement2008 < 2655   to the left,  improve=21.75660, (0 missing)
## 
## Node number 28: 13123 observations,    complexity param=0.00359764
##   predicted class=B1  expected loss=0.5360055  P(node) =0.04775421
##     class counts:  6089  4435  1751   763    85
##    probabilities: 0.464 0.338 0.133 0.058 0.006 
##   left son=56 (9625 obs) right son=57 (3498 obs)
##   Primary splits:
##       arthritis     < 0.5    to the left,  improve=126.28480, (0 missing)
##       ihd           < 0.5    to the left,  improve= 70.76778, (0 missing)
##       depression    < 0.5    to the left,  improve= 68.94332, (0 missing)
##       osteoporosis  < 0.5    to the left,  improve= 46.31934, (0 missing)
##       heart.failure < 0.5    to the left,  improve= 30.26771, (0 missing)
## 
## Node number 29: 2594 observations,    complexity param=6.167383e-05
##   predicted class=B2  expected loss=0.5932922  P(node) =0.009439489
##     class counts:   662  1055   614   236    27
##    probabilities: 0.255 0.407 0.237 0.091 0.010 
##   left son=58 (1000 obs) right son=59 (1594 obs)
##   Primary splits:
##       reimbursement2008 < 5770   to the left,  improve=8.464458, (0 missing)
##       arthritis         < 0.5    to the left,  improve=7.371565, (0 missing)
##       ihd               < 0.5    to the left,  improve=5.410820, (0 missing)
##       copd              < 0.5    to the left,  improve=5.301788, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.070575, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.823, adj=0.542, (0 split)
## 
## Node number 30: 25067 observations,    complexity param=0.0009879673
##   predicted class=B2  expected loss=0.57091  P(node) =0.09121807
##     class counts:  7517 10756  4691  1917   186
##    probabilities: 0.300 0.429 0.187 0.076 0.007 
##   left son=60 (15178 obs) right son=61 (9889 obs)
##   Primary splits:
##       arthritis     < 0.5    to the left,  improve=169.25970, (0 missing)
##       cancer        < 0.5    to the left,  improve= 99.57556, (0 missing)
##       ihd           < 0.5    to the left,  improve= 68.28883, (0 missing)
##       depression    < 0.5    to the left,  improve= 61.94482, (0 missing)
##       heart.failure < 0.5    to the left,  improve= 42.19646, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 66495  to the left,  agree=0.606, adj=0.001, (0 split)
## 
## Node number 31: 28734 observations,    complexity param=0.0002036818
##   predicted class=B2  expected loss=0.5964711  P(node) =0.1045622
##     class counts:  4704 11595  6672  4893   870
##    probabilities: 0.164 0.404 0.232 0.170 0.030 
##   left son=62 (16249 obs) right son=63 (12485 obs)
##   Primary splits:
##       reimbursement2008 < 15395  to the left,  improve=177.49270, (0 missing)
##       bucket2008        < 3.5    to the left,  improve=170.28940, (0 missing)
##       arthritis         < 0.5    to the left,  improve=101.31920, (0 missing)
##       heart.failure     < 0.5    to the left,  improve= 62.82321, (0 missing)
##       ihd               < 0.5    to the left,  improve= 55.35075, (0 missing)
##   Surrogate splits:
##       bucket2008 < 3.5    to the left,  agree=0.924, adj=0.826, (0 split)
##       copd       < 0.5    to the left,  agree=0.609, adj=0.101, (0 split)
##       stroke     < 0.5    to the left,  agree=0.605, adj=0.091, (0 split)
##       cancer     < 0.5    to the left,  agree=0.580, adj=0.033, (0 split)
##       alzheimers < 0.5    to the left,  agree=0.569, adj=0.008, (0 split)
## 
## Node number 50: 9007 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.4490951  P(node) =0.03277621
##     class counts:  4962  2540  1087   378    40
##    probabilities: 0.551 0.282 0.121 0.042 0.004 
##   left son=100 (4935 obs) right son=101 (4072 obs)
##   Primary splits:
##       reimbursement2008 < 1875   to the left,  improve=14.670650, (0 missing)
##       cancer            < 0.5    to the left,  improve=12.077140, (0 missing)
##       arthritis         < 0.5    to the left,  improve= 9.470091, (0 missing)
##       heart.failure     < 0.5    to the left,  improve= 7.308909, (0 missing)
##       depression        < 0.5    to the left,  improve= 6.801973, (0 missing)
##   Surrogate splits:
##       stroke < 0.5    to the left,  agree=0.549, adj=0.003, (0 split)
##       age    < 29.5   to the right, agree=0.548, adj=0.001, (0 split)
## 
## Node number 51: 2244 observations,    complexity param=9.409212e-05
##   predicted class=B1  expected loss=0.5356506  P(node) =0.00816585
##     class counts:  1042   739   308   144    11
##    probabilities: 0.464 0.329 0.137 0.064 0.005 
##   left son=102 (992 obs) right son=103 (1252 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the left,  improve=7.795458, (0 missing)
##       arthritis         < 0.5    to the left,  improve=7.027320, (0 missing)
##       ihd               < 0.5    to the left,  improve=4.964222, (0 missing)
##       reimbursement2008 < 1735   to the left,  improve=4.132280, (0 missing)
##       cancer            < 0.5    to the left,  improve=3.835396, (0 missing)
##   Surrogate splits:
##       ihd < 0.5    to the left,  agree=0.565, adj=0.016, (0 split)
##       age < 33.5   to the left,  agree=0.559, adj=0.002, (0 split)
## 
## Node number 52: 5554 observations,    complexity param=5.165842e-05
##   predicted class=B1  expected loss=0.4449046  P(node) =0.02021084
##     class counts:  3083  1647   580   217    27
##    probabilities: 0.555 0.297 0.104 0.039 0.005 
##   left son=104 (2348 obs) right son=105 (3206 obs)
##   Primary splits:
##       ihd           < 0.5    to the left,  improve=13.118310, (0 missing)
##       depression    < 0.5    to the left,  improve=12.689550, (0 missing)
##       kidney        < 0.5    to the left,  improve= 9.684755, (0 missing)
##       copd          < 0.5    to the left,  improve= 9.145592, (0 missing)
##       heart.failure < 0.5    to the left,  improve= 8.228139, (0 missing)
##   Surrogate splits:
##       age               < 28.5   to the left,  agree=0.579, adj=0.004, (0 split)
##       reimbursement2008 < 2185   to the left,  agree=0.578, adj=0.001, (0 split)
## 
## Node number 53: 1583 observations,    complexity param=0.0001051618
##   predicted class=B1  expected loss=0.5615919  P(node) =0.00576049
##     class counts:   694   586   214    83     6
##    probabilities: 0.438 0.370 0.135 0.052 0.004 
##   left son=106 (1525 obs) right son=107 (58 obs)
##   Primary splits:
##       stroke            < 0.5    to the left,  improve=5.133391, (0 missing)
##       reimbursement2008 < 2725   to the left,  improve=3.164238, (0 missing)
##       cancer            < 0.5    to the left,  improve=2.451745, (0 missing)
##       copd              < 0.5    to the left,  improve=2.436381, (0 missing)
##       depression        < 0.5    to the left,  improve=1.979459, (0 missing)
## 
## Node number 54: 8413 observations,    complexity param=0.0004372516
##   predicted class=B1  expected loss=0.5530726  P(node) =0.03061466
##     class counts:  3760  2943  1225   438    47
##    probabilities: 0.447 0.350 0.146 0.052 0.006 
##   left son=108 (4375 obs) right son=109 (4038 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the left,  improve=25.12070, (0 missing)
##       ihd               < 0.5    to the left,  improve=19.50225, (0 missing)
##       kidney            < 0.5    to the left,  improve=18.23799, (0 missing)
##       depression        < 0.5    to the left,  improve=14.07225, (0 missing)
##       reimbursement2008 < 2615   to the left,  improve=12.21338, (0 missing)
##   Surrogate splits:
##       kidney     < 0.5    to the left,  agree=0.569, adj=0.103, (0 split)
##       copd       < 0.5    to the left,  agree=0.568, adj=0.100, (0 split)
##       alzheimers < 0.5    to the left,  agree=0.546, adj=0.054, (0 split)
##       ihd        < 0.5    to the left,  agree=0.544, adj=0.050, (0 split)
##       stroke     < 0.5    to the left,  agree=0.536, adj=0.034, (0 split)
## 
## Node number 55: 3671 observations,    complexity param=0.0002988809
##   predicted class=B2  expected loss=0.579951  P(node) =0.01335866
##     class counts:  1358  1542   537   210    24
##    probabilities: 0.370 0.420 0.146 0.057 0.007 
##   left son=110 (2068 obs) right son=111 (1603 obs)
##   Primary splits:
##       reimbursement2008 < 2665   to the left,  improve=10.442080, (0 missing)
##       cancer            < 0.5    to the left,  improve= 4.234333, (0 missing)
##       ihd               < 0.5    to the left,  improve= 4.129116, (0 missing)
##       kidney            < 0.5    to the left,  improve= 3.679214, (0 missing)
##       copd              < 0.5    to the left,  improve= 3.281268, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the left,  agree=0.638, adj=0.170, (0 split)
##       cancer     < 0.5    to the left,  agree=0.566, adj=0.006, (0 split)
##       age        < 26.5   to the right, agree=0.564, adj=0.001, (0 split)
##       stroke     < 0.5    to the left,  agree=0.564, adj=0.001, (0 split)
## 
## Node number 56: 9625 observations,    complexity param=0.0001217663
##   predicted class=B1  expected loss=0.4874805  P(node) =0.03502509
##     class counts:  4933  2954  1162   520    56
##    probabilities: 0.513 0.307 0.121 0.054 0.006 
##   left son=112 (3135 obs) right son=113 (6490 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=51.18602, (0 missing)
##       depression        < 0.5    to the left,  improve=46.82343, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=27.25528, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=25.54800, (0 missing)
##       reimbursement2008 < 6615   to the left,  improve=12.84564, (0 missing)
## 
## Node number 57: 3498 observations,    complexity param=0.0004372516
##   predicted class=B2  expected loss=0.5766152  P(node) =0.01272912
##     class counts:  1156  1481   589   243    29
##    probabilities: 0.330 0.423 0.168 0.069 0.008 
##   left son=114 (2340 obs) right son=115 (1158 obs)
##   Primary splits:
##       reimbursement2008 < 8525   to the left,  improve=12.263650, (0 missing)
##       depression        < 0.5    to the left,  improve=10.454350, (0 missing)
##       bucket2008        < 2.5    to the left,  improve= 9.052395, (0 missing)
##       copd              < 0.5    to the left,  improve= 8.848663, (0 missing)
##       ihd               < 0.5    to the left,  improve= 8.087092, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.979, adj=0.935, (0 split)
##       kidney     < 0.5    to the left,  agree=0.692, adj=0.069, (0 split)
##       stroke     < 0.5    to the left,  agree=0.680, adj=0.033, (0 split)
## 
## Node number 58: 1000 observations
##   predicted class=B2  expected loss=0.562  P(node) =0.00363897
##     class counts:   296   438   191    70     5
##    probabilities: 0.296 0.438 0.191 0.070 0.005 
## 
## Node number 59: 1594 observations,    complexity param=6.167383e-05
##   predicted class=B2  expected loss=0.6129235  P(node) =0.005800519
##     class counts:   366   617   423   166    22
##    probabilities: 0.230 0.387 0.265 0.104 0.014 
##   left son=118 (1054 obs) right son=119 (540 obs)
##   Primary splits:
##       reimbursement2008 < 8645   to the right, improve=7.014383, (0 missing)
##       arthritis         < 0.5    to the left,  improve=5.636989, (0 missing)
##       bucket2008        < 2.5    to the right, improve=4.256675, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=3.245615, (0 missing)
##       ihd               < 0.5    to the left,  improve=2.672736, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.949, adj=0.848, (0 split)
##       age        < 27.5   to the right, agree=0.662, adj=0.002, (0 split)
## 
## Node number 60: 15178 observations,    complexity param=0.0009879673
##   predicted class=B2  expected loss=0.6047569  P(node) =0.05523229
##     class counts:  5388  5999  2649  1047    95
##    probabilities: 0.355 0.395 0.175 0.069 0.006 
##   left son=120 (12572 obs) right son=121 (2606 obs)
##   Primary splits:
##       cancer        < 0.5    to the left,  improve=92.65854, (0 missing)
##       ihd           < 0.5    to the left,  improve=43.72992, (0 missing)
##       depression    < 0.5    to the left,  improve=36.05906, (0 missing)
##       heart.failure < 0.5    to the left,  improve=30.26654, (0 missing)
##       copd          < 0.5    to the left,  improve=25.73984, (0 missing)
## 
## Node number 61: 9889 observations,    complexity param=6.918538e-05
##   predicted class=B2  expected loss=0.5189605  P(node) =0.03598578
##     class counts:  2129  4757  2042   870    91
##    probabilities: 0.215 0.481 0.206 0.088 0.009 
##   left son=122 (5134 obs) right son=123 (4755 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=18.84327, (0 missing)
##       cancer            < 0.5    to the left,  improve=17.45891, (0 missing)
##       ihd               < 0.5    to the left,  improve=13.35120, (0 missing)
##       reimbursement2008 < 9795   to the left,  improve=12.33086, (0 missing)
##       copd              < 0.5    to the left,  improve=12.26415, (0 missing)
##   Surrogate splits:
##       alzheimers        < 0.5    to the left,  agree=0.564, adj=0.093, (0 split)
##       copd              < 0.5    to the left,  agree=0.546, adj=0.056, (0 split)
##       reimbursement2008 < 5815   to the left,  agree=0.542, adj=0.048, (0 split)
##       age               < 64.5   to the right, agree=0.537, adj=0.037, (0 split)
##       bucket2008        < 2.5    to the left,  agree=0.536, adj=0.036, (0 split)
## 
## Node number 62: 16249 observations,    complexity param=0.0001411382
##   predicted class=B2  expected loss=0.5619423  P(node) =0.05912963
##     class counts:  3113  7118  3819  1946   253
##    probabilities: 0.192 0.438 0.235 0.120 0.016 
##   left son=124 (9424 obs) right son=125 (6825 obs)
##   Primary splits:
##       arthritis         < 0.5    to the left,  improve=70.60653, (0 missing)
##       cancer            < 0.5    to the left,  improve=30.24922, (0 missing)
##       ihd               < 0.5    to the left,  improve=29.86941, (0 missing)
##       reimbursement2008 < 5665   to the left,  improve=23.89268, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=21.55872, (0 missing)
## 
## Node number 63: 12485 observations,    complexity param=0.0002036818
##   predicted class=B2  expected loss=0.6414097  P(node) =0.04543255
##     class counts:  1591  4477  2853  2947   617
##    probabilities: 0.127 0.359 0.229 0.236 0.049 
##   left son=126 (5402 obs) right son=127 (7083 obs)
##   Primary splits:
##       arthritis         < 0.5    to the right, improve=35.40534, (0 missing)
##       cancer            < 0.5    to the left,  improve=26.78171, (0 missing)
##       reimbursement2008 < 26625  to the left,  improve=24.60405, (0 missing)
##       depression        < 0.5    to the left,  improve=23.29796, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=17.01274, (0 missing)
##   Surrogate splits:
##       age               < 28.5   to the left,  agree=0.568, adj=0.002, (0 split)
##       reimbursement2008 < 15435  to the left,  agree=0.568, adj=0.001, (0 split)
## 
## Node number 100: 4935 observations
##   predicted class=B1  expected loss=0.4196555  P(node) =0.01795832
##     class counts:  2864  1294   550   205    22
##    probabilities: 0.580 0.262 0.111 0.042 0.004 
## 
## Node number 101: 4072 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.4847741  P(node) =0.01481789
##     class counts:  2098  1246   537   173    18
##    probabilities: 0.515 0.306 0.132 0.042 0.004 
##   left son=202 (3786 obs) right son=203 (286 obs)
##   Primary splits:
##       cancer        < 0.5    to the left,  improve=5.937439, (0 missing)
##       arthritis     < 0.5    to the left,  improve=5.625805, (0 missing)
##       copd          < 0.5    to the left,  improve=3.348444, (0 missing)
##       ihd           < 0.5    to the left,  improve=3.030239, (0 missing)
##       heart.failure < 0.5    to the left,  improve=2.851779, (0 missing)
## 
## Node number 102: 992 observations
##   predicted class=B1  expected loss=0.4808468  P(node) =0.003609859
##     class counts:   515   292   126    57     2
##    probabilities: 0.519 0.294 0.127 0.057 0.002 
## 
## Node number 103: 1252 observations,    complexity param=9.409212e-05
##   predicted class=B1  expected loss=0.5790735  P(node) =0.004555991
##     class counts:   527   447   182    87     9
##    probabilities: 0.421 0.357 0.145 0.069 0.007 
##   left son=206 (904 obs) right son=207 (348 obs)
##   Primary splits:
##       arthritis         < 0.5    to the left,  improve=7.739842, (0 missing)
##       age               < 93.5   to the left,  improve=3.754099, (0 missing)
##       cancer            < 0.5    to the left,  improve=3.514161, (0 missing)
##       reimbursement2008 < 1955   to the left,  improve=3.377454, (0 missing)
##       ihd               < 0.5    to the left,  improve=1.751139, (0 missing)
##   Surrogate splits:
##       age < 30.5   to the right, agree=0.724, adj=0.006, (0 split)
## 
## Node number 104: 2348 observations
##   predicted class=B1  expected loss=0.3973595  P(node) =0.008544303
##     class counts:  1415   632   217    72    12
##    probabilities: 0.603 0.269 0.092 0.031 0.005 
## 
## Node number 105: 3206 observations,    complexity param=5.165842e-05
##   predicted class=B1  expected loss=0.4797255  P(node) =0.01166654
##     class counts:  1668  1015   363   145    15
##    probabilities: 0.520 0.317 0.113 0.045 0.005 
##   left son=210 (2325 obs) right son=211 (881 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=8.135493, (0 missing)
##       kidney            < 0.5    to the left,  improve=5.219511, (0 missing)
##       reimbursement2008 < 2785   to the left,  improve=4.205524, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.201394, (0 missing)
##       copd              < 0.5    to the left,  improve=3.002159, (0 missing)
##   Surrogate splits:
##       age < 29.5   to the right, agree=0.726, adj=0.003, (0 split)
## 
## Node number 106: 1525 observations,    complexity param=0.0001051618
##   predicted class=B1  expected loss=0.5534426  P(node) =0.00554943
##     class counts:   681   554   202    82     6
##    probabilities: 0.447 0.363 0.132 0.054 0.004 
##   left son=212 (1438 obs) right son=213 (87 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=2.548424, (0 missing)
##       reimbursement2008 < 2715   to the right, improve=2.513748, (0 missing)
##       copd              < 0.5    to the left,  improve=1.973703, (0 missing)
##       depression        < 0.5    to the left,  improve=1.853940, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.632947, (0 missing)
## 
## Node number 107: 58 observations
##   predicted class=B2  expected loss=0.4482759  P(node) =0.0002110603
##     class counts:    13    32    12     1     0
##    probabilities: 0.224 0.552 0.207 0.017 0.000 
## 
## Node number 108: 4375 observations,    complexity param=0.0002435326
##   predicted class=B1  expected loss=0.5074286  P(node) =0.0159205
##     class counts:  2155  1478   555   170    17
##    probabilities: 0.493 0.338 0.127 0.039 0.004 
##   left son=216 (3992 obs) right son=217 (383 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=10.015540, (0 missing)
##       ihd               < 0.5    to the left,  improve= 9.488719, (0 missing)
##       depression        < 0.5    to the left,  improve= 7.316301, (0 missing)
##       reimbursement2008 < 2615   to the left,  improve= 5.949976, (0 missing)
##       copd              < 0.5    to the left,  improve= 5.117423, (0 missing)
## 
## Node number 109: 4038 observations,    complexity param=0.0004372516
##   predicted class=B1  expected loss=0.602526  P(node) =0.01469416
##     class counts:  1605  1465   670   268    30
##    probabilities: 0.397 0.363 0.166 0.066 0.007 
##   left son=218 (2819 obs) right son=219 (1219 obs)
##   Primary splits:
##       kidney            < 0.5    to the left,  improve=10.392200, (0 missing)
##       reimbursement2008 < 2455   to the left,  improve= 6.028802, (0 missing)
##       ihd               < 0.5    to the left,  improve= 5.795095, (0 missing)
##       depression        < 0.5    to the left,  improve= 5.214940, (0 missing)
##       stroke            < 0.5    to the left,  improve= 3.343262, (0 missing)
## 
## Node number 110: 2068 observations,    complexity param=0.0002767415
##   predicted class=B1  expected loss=0.5918762  P(node) =0.007525391
##     class counts:   844   817   280   117    10
##    probabilities: 0.408 0.395 0.135 0.057 0.005 
##   left son=220 (517 obs) right son=221 (1551 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=3.581883, (0 missing)
##       reimbursement2008 < 2305   to the left,  improve=3.255344, (0 missing)
##       cancer            < 0.5    to the left,  improve=3.097089, (0 missing)
##       age               < 54.5   to the left,  improve=1.964830, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.730688, (0 missing)
## 
## Node number 111: 1603 observations
##   predicted class=B2  expected loss=0.547723  P(node) =0.00583327
##     class counts:   514   725   257    93    14
##    probabilities: 0.321 0.452 0.160 0.058 0.009 
## 
## Node number 112: 3135 observations,    complexity param=7.19528e-05
##   predicted class=B1  expected loss=0.3974482  P(node) =0.01140817
##     class counts:  1889   825   298   113    10
##    probabilities: 0.603 0.263 0.095 0.036 0.003 
##   left son=224 (2292 obs) right son=225 (843 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=19.892930, (0 missing)
##       reimbursement2008 < 9505   to the right, improve=15.211730, (0 missing)
##       bucket2008        < 2.5    to the right, improve=13.054300, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=10.317040, (0 missing)
##       age               < 92.5   to the left,  improve= 3.244996, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 60755  to the left,  agree=0.731, adj=0.001, (0 split)
## 
## Node number 113: 6490 observations,    complexity param=0.0001217663
##   predicted class=B1  expected loss=0.5309707  P(node) =0.02361692
##     class counts:  3044  2129   864   407    46
##    probabilities: 0.469 0.328 0.133 0.063 0.007 
##   left son=226 (4266 obs) right son=227 (2224 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=22.130520, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=12.472230, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=12.135520, (0 missing)
##       reimbursement2008 < 6615   to the left,  improve=10.028930, (0 missing)
##       bucket2008        < 2.5    to the left,  improve= 8.000565, (0 missing)
##   Surrogate splits:
##       age               < 34.5   to the right, agree=0.658, adj=0.003, (0 split)
##       reimbursement2008 < 115145 to the left,  agree=0.658, adj=0.001, (0 split)
## 
## Node number 114: 2340 observations,    complexity param=5.313437e-05
##   predicted class=B2  expected loss=0.542735  P(node) =0.008515191
##     class counts:   720  1070   391   144    15
##    probabilities: 0.308 0.457 0.167 0.062 0.006 
##   left son=228 (1359 obs) right son=229 (981 obs)
##   Primary splits:
##       reimbursement2008 < 4645   to the left,  improve=5.782135, (0 missing)
##       ihd               < 0.5    to the left,  improve=5.431632, (0 missing)
##       depression        < 0.5    to the left,  improve=4.505952, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=3.336155, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=3.247654, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.613, adj=0.076, (0 split)
##       copd       < 0.5    to the left,  agree=0.606, adj=0.059, (0 split)
##       kidney     < 0.5    to the left,  agree=0.586, adj=0.013, (0 split)
##       age        < 91.5   to the left,  agree=0.585, adj=0.011, (0 split)
##       stroke     < 0.5    to the left,  agree=0.585, adj=0.009, (0 split)
## 
## Node number 115: 1158 observations,    complexity param=0.0004372516
##   predicted class=B1  expected loss=0.6234888  P(node) =0.004213928
##     class counts:   436   411   198    99    14
##    probabilities: 0.377 0.355 0.171 0.085 0.012 
##   left son=230 (714 obs) right son=231 (444 obs)
##   Primary splits:
##       copd              < 0.5    to the left,  improve=13.168040, (0 missing)
##       depression        < 0.5    to the left,  improve= 8.948306, (0 missing)
##       kidney            < 0.5    to the left,  improve= 6.276303, (0 missing)
##       ihd               < 0.5    to the left,  improve= 5.293866, (0 missing)
##       reimbursement2008 < 14980  to the left,  improve= 4.056180, (0 missing)
##   Surrogate splits:
##       age               < 94.5   to the left,  agree=0.626, adj=0.025, (0 split)
##       reimbursement2008 < 72745  to the left,  agree=0.620, adj=0.009, (0 split)
## 
## Node number 118: 1054 observations,    complexity param=6.167383e-05
##   predicted class=B2  expected loss=0.6223909  P(node) =0.003835475
##     class counts:   281   398   250   109    16
##    probabilities: 0.267 0.378 0.237 0.103 0.015 
##   left son=236 (745 obs) right son=237 (309 obs)
##   Primary splits:
##       arthritis         < 0.5    to the left,  improve=8.492364, (0 missing)
##       ihd               < 0.5    to the left,  improve=3.739184, (0 missing)
##       depression        < 0.5    to the left,  improve=2.714506, (0 missing)
##       copd              < 0.5    to the left,  improve=2.704564, (0 missing)
##       reimbursement2008 < 67610  to the left,  improve=2.665770, (0 missing)
##   Surrogate splits:
##       age < 29.5   to the right, agree=0.708, adj=0.003, (0 split)
## 
## Node number 119: 540 observations,    complexity param=6.167383e-05
##   predicted class=B2  expected loss=0.5944444  P(node) =0.001965044
##     class counts:    85   219   173    57     6
##    probabilities: 0.157 0.406 0.320 0.106 0.011 
##   left son=238 (243 obs) right son=239 (297 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the right, improve=3.144781, (0 missing)
##       reimbursement2008 < 7455   to the left,  improve=1.665302, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.352183, (0 missing)
##       age               < 86.5   to the right, improve=1.232072, (0 missing)
##       arthritis         < 0.5    to the right, improve=1.028824, (0 missing)
##   Surrogate splits:
##       copd       < 0.5    to the right, agree=0.604, adj=0.119, (0 split)
##       kidney     < 0.5    to the right, agree=0.585, adj=0.078, (0 split)
##       stroke     < 0.5    to the right, agree=0.583, adj=0.074, (0 split)
##       arthritis  < 0.5    to the right, agree=0.576, adj=0.058, (0 split)
##       depression < 0.5    to the right, agree=0.572, adj=0.049, (0 split)
## 
## Node number 120: 12572 observations,    complexity param=0.0009879673
##   predicted class=B2  expected loss=0.613188  P(node) =0.04574914
##     class counts:  4844  4863  2000   791    74
##    probabilities: 0.385 0.387 0.159 0.063 0.006 
##   left son=240 (2617 obs) right son=241 (9955 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=36.80981, (0 missing)
##       depression        < 0.5    to the left,  improve=36.47326, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=27.52215, (0 missing)
##       copd              < 0.5    to the left,  improve=21.85222, (0 missing)
##       reimbursement2008 < 8955   to the left,  improve=19.34797, (0 missing)
## 
## Node number 121: 2606 observations
##   predicted class=B2  expected loss=0.5640829  P(node) =0.009483157
##     class counts:   544  1136   649   256    21
##    probabilities: 0.209 0.436 0.249 0.098 0.008 
## 
## Node number 122: 5134 observations,    complexity param=6.918538e-05
##   predicted class=B2  expected loss=0.5190884  P(node) =0.01868247
##     class counts:  1277  2469   936   412    40
##    probabilities: 0.249 0.481 0.182 0.080 0.008 
##   left son=244 (4305 obs) right son=245 (829 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=12.348810, (0 missing)
##       reimbursement2008 < 9985   to the left,  improve=11.590150, (0 missing)
##       bucket2008        < 2.5    to the left,  improve= 7.979608, (0 missing)
##       ihd               < 0.5    to the left,  improve= 7.512372, (0 missing)
##       copd              < 0.5    to the left,  improve= 7.186891, (0 missing)
## 
## Node number 123: 4755 observations
##   predicted class=B2  expected loss=0.5188223  P(node) =0.0173033
##     class counts:   852  2288  1106   458    51
##    probabilities: 0.179 0.481 0.233 0.096 0.011 
## 
## Node number 124: 9424 observations,    complexity param=0.0001411382
##   predicted class=B2  expected loss=0.5992148  P(node) =0.03429366
##     class counts:  2192  3777  2139  1156   160
##    probabilities: 0.233 0.401 0.227 0.123 0.017 
##   left son=248 (7786 obs) right son=249 (1638 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=31.06099, (0 missing)
##       ihd               < 0.5    to the left,  improve=19.93184, (0 missing)
##       depression        < 0.5    to the left,  improve=16.57581, (0 missing)
##       reimbursement2008 < 6325   to the left,  improve=12.91187, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=10.82187, (0 missing)
## 
## Node number 125: 6825 observations
##   predicted class=B2  expected loss=0.5104762  P(node) =0.02483597
##     class counts:   921  3341  1680   790    93
##    probabilities: 0.135 0.490 0.246 0.116 0.014 
## 
## Node number 126: 5402 observations,    complexity param=7.748763e-05
##   predicted class=B2  expected loss=0.5960755  P(node) =0.01965772
##     class counts:   509  2182  1310  1186   215
##    probabilities: 0.094 0.404 0.243 0.220 0.040 
##   left son=252 (3345 obs) right son=253 (2057 obs)
##   Primary splits:
##       reimbursement2008 < 34925  to the left,  improve=14.212070, (0 missing)
##       copd              < 0.5    to the left,  improve=10.384850, (0 missing)
##       depression        < 0.5    to the left,  improve= 8.104595, (0 missing)
##       cancer            < 0.5    to the right, improve= 6.743072, (0 missing)
##       heart.failure     < 0.5    to the left,  improve= 6.417519, (0 missing)
##   Surrogate splits:
##       bucket2008 < 4.5    to the left,  agree=0.776, adj=0.413, (0 split)
## 
## Node number 127: 7083 observations,    complexity param=0.0002036818
##   predicted class=B2  expected loss=0.6759848  P(node) =0.02577483
##     class counts:  1082  2295  1543  1761   402
##    probabilities: 0.153 0.324 0.218 0.249 0.057 
##   left son=254 (5298 obs) right son=255 (1785 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=21.09129, (0 missing)
##       depression        < 0.5    to the left,  improve=19.29947, (0 missing)
##       reimbursement2008 < 26625  to the left,  improve=15.18952, (0 missing)
##       copd              < 0.5    to the left,  improve=14.68870, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=12.81802, (0 missing)
## 
## Node number 202: 3786 observations
##   predicted class=B1  expected loss=0.4772847  P(node) =0.01377714
##     class counts:  1979  1131   501   157    18
##    probabilities: 0.523 0.299 0.132 0.041 0.005 
## 
## Node number 203: 286 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5839161  P(node) =0.001040746
##     class counts:   119   115    36    16     0
##    probabilities: 0.416 0.402 0.126 0.056 0.000 
##   left son=406 (128 obs) right son=407 (158 obs)
##   Primary splits:
##       age               < 73.5   to the left,  improve=2.9724540, (0 missing)
##       reimbursement2008 < 2005   to the left,  improve=1.9802050, (0 missing)
##       depression        < 0.5    to the left,  improve=0.5460014, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.4144954, (0 missing)
##       ihd               < 0.5    to the left,  improve=0.3767582, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 1945   to the left,  agree=0.580, adj=0.063, (0 split)
##       arthritis         < 0.5    to the right, agree=0.563, adj=0.023, (0 split)
## 
## Node number 206: 904 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5376106  P(node) =0.003289629
##     class counts:   418   304   119    57     6
##    probabilities: 0.462 0.336 0.132 0.063 0.007 
##   left son=412 (270 obs) right son=413 (634 obs)
##   Primary splits:
##       reimbursement2008 < 1735   to the left,  improve=3.8438620, (0 missing)
##       age               < 93.5   to the left,  improve=3.6681650, (0 missing)
##       ihd               < 0.5    to the left,  improve=3.2669730, (0 missing)
##       cancer            < 0.5    to the left,  improve=3.0869480, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.7446912, (0 missing)
##   Surrogate splits:
##       age < 29     to the left,  agree=0.702, adj=0.004, (0 split)
## 
## Node number 207: 348 observations
##   predicted class=B2  expected loss=0.5890805  P(node) =0.001266362
##     class counts:   109   143    63    30     3
##    probabilities: 0.313 0.411 0.181 0.086 0.009 
## 
## Node number 210: 2325 observations
##   predicted class=B1  expected loss=0.4541935  P(node) =0.008460606
##     class counts:  1269   700   245    99    12
##    probabilities: 0.546 0.301 0.105 0.043 0.005 
## 
## Node number 211: 881 observations,    complexity param=5.165842e-05
##   predicted class=B1  expected loss=0.5471056  P(node) =0.003205933
##     class counts:   399   315   118    46     3
##    probabilities: 0.453 0.358 0.134 0.052 0.003 
##   left son=422 (763 obs) right son=423 (118 obs)
##   Primary splits:
##       kidney            < 0.5    to the left,  improve=3.122415, (0 missing)
##       age               < 78.5   to the right, improve=2.656467, (0 missing)
##       reimbursement2008 < 2205   to the right, improve=1.600090, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.074836, (0 missing)
##       bucket2008        < 1.5    to the left,  improve=1.071176, (0 missing)
## 
## Node number 212: 1438 observations,    complexity param=8.855729e-05
##   predicted class=B1  expected loss=0.5452017  P(node) =0.00523284
##     class counts:   654   515   187    76     6
##    probabilities: 0.455 0.358 0.130 0.053 0.004 
##   left son=424 (495 obs) right son=425 (943 obs)
##   Primary splits:
##       reimbursement2008 < 2715   to the right, improve=2.835023, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.879898, (0 missing)
##       copd              < 0.5    to the left,  improve=1.857999, (0 missing)
##       age               < 40.5   to the right, improve=1.802592, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.761837, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the right, agree=0.713, adj=0.166, (0 split)
##       age        < 28.5   to the left,  agree=0.656, adj=0.002, (0 split)
## 
## Node number 213: 87 observations
##   predicted class=B2  expected loss=0.5517241  P(node) =0.0003165904
##     class counts:    27    39    15     6     0
##    probabilities: 0.310 0.448 0.172 0.069 0.000 
## 
## Node number 216: 3992 observations,    complexity param=6.918538e-05
##   predicted class=B1  expected loss=0.495491  P(node) =0.01452677
##     class counts:  2014  1315   497   153    13
##    probabilities: 0.505 0.329 0.124 0.038 0.003 
##   left son=432 (1265 obs) right son=433 (2727 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=7.867939, (0 missing)
##       depression        < 0.5    to the left,  improve=6.016589, (0 missing)
##       copd              < 0.5    to the left,  improve=5.402587, (0 missing)
##       kidney            < 0.5    to the left,  improve=3.916699, (0 missing)
##       reimbursement2008 < 2615   to the left,  improve=3.836002, (0 missing)
## 
## Node number 217: 383 observations,    complexity param=0.0001826494
##   predicted class=B2  expected loss=0.5744125  P(node) =0.001393726
##     class counts:   141   163    58    17     4
##    probabilities: 0.368 0.426 0.151 0.044 0.010 
##   left son=434 (238 obs) right son=435 (145 obs)
##   Primary splits:
##       reimbursement2008 < 2705   to the left,  improve=4.9624930, (0 missing)
##       depression        < 0.5    to the left,  improve=3.2303380, (0 missing)
##       age               < 67.5   to the right, improve=2.3511250, (0 missing)
##       ihd               < 0.5    to the left,  improve=1.5735720, (0 missing)
##       bucket2008        < 1.5    to the left,  improve=0.9813303, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the left,  agree=0.681, adj=0.159, (0 split)
##       age        < 45.5   to the right, agree=0.624, adj=0.007, (0 split)
## 
## Node number 218: 2819 observations,    complexity param=0.0001129105
##   predicted class=B1  expected loss=0.5746719  P(node) =0.01025826
##     class counts:  1199   980   439   183    18
##    probabilities: 0.425 0.348 0.156 0.065 0.006 
##   left son=436 (635 obs) right son=437 (2184 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=6.072389, (0 missing)
##       reimbursement2008 < 2325   to the left,  improve=3.797765, (0 missing)
##       age               < 40.5   to the right, improve=3.110525, (0 missing)
##       depression        < 0.5    to the left,  improve=2.993563, (0 missing)
##       stroke            < 0.5    to the left,  improve=2.412511, (0 missing)
## 
## Node number 219: 1219 observations,    complexity param=8.855729e-05
##   predicted class=B2  expected loss=0.6021329  P(node) =0.004435905
##     class counts:   406   485   231    85    12
##    probabilities: 0.333 0.398 0.189 0.070 0.010 
##   left son=438 (613 obs) right son=439 (606 obs)
##   Primary splits:
##       reimbursement2008 < 2615   to the left,  improve=4.2080810, (0 missing)
##       age               < 98.5   to the right, improve=2.1482090, (0 missing)
##       depression        < 0.5    to the left,  improve=1.6601240, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.8099205, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=0.7434054, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the left,  agree=0.579, adj=0.153, (0 split)
##       depression < 0.5    to the left,  agree=0.523, adj=0.041, (0 split)
##       stroke     < 0.5    to the left,  agree=0.522, adj=0.038, (0 split)
##       age        < 65.5   to the right, agree=0.519, adj=0.033, (0 split)
##       cancer     < 0.5    to the left,  agree=0.514, adj=0.021, (0 split)
## 
## Node number 220: 517 observations,    complexity param=7.19528e-05
##   predicted class=B1  expected loss=0.5299807  P(node) =0.001881348
##     class counts:   243   191    57    25     1
##    probabilities: 0.470 0.369 0.110 0.048 0.002 
##   left son=440 (143 obs) right son=441 (374 obs)
##   Primary splits:
##       reimbursement2008 < 2295   to the left,  improve=6.0966680, (0 missing)
##       cancer            < 0.5    to the left,  improve=2.5628030, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.9493160, (0 missing)
##       age               < 44.5   to the right, improve=1.5968610, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.9005685, (0 missing)
##   Surrogate splits:
##       age < 98.5   to the right, agree=0.729, adj=0.021, (0 split)
## 
## Node number 221: 1551 observations,    complexity param=9.962695e-05
##   predicted class=B2  expected loss=0.5963894  P(node) =0.005644043
##     class counts:   601   626   223    92     9
##    probabilities: 0.387 0.404 0.144 0.059 0.006 
##   left son=442 (18 obs) right son=443 (1533 obs)
##   Primary splits:
##       age    < 35     to the left,  improve=3.0170030, (0 missing)
##       kidney < 0.5    to the left,  improve=2.3281310, (0 missing)
##       cancer < 0.5    to the left,  improve=1.5502140, (0 missing)
##       stroke < 0.5    to the left,  improve=1.1903410, (0 missing)
##       copd   < 0.5    to the left,  improve=0.9727402, (0 missing)
## 
## Node number 224: 2292 observations
##   predicted class=B1  expected loss=0.3582024  P(node) =0.00834052
##     class counts:  1471   549   183    79    10
##    probabilities: 0.642 0.240 0.080 0.034 0.004 
## 
## Node number 225: 843 observations,    complexity param=7.19528e-05
##   predicted class=B1  expected loss=0.5041518  P(node) =0.003067652
##     class counts:   418   276   115    34     0
##    probabilities: 0.496 0.327 0.136 0.040 0.000 
##   left son=450 (810 obs) right son=451 (33 obs)
##   Primary splits:
##       age               < 92.5   to the left,  improve=5.7055350, (0 missing)
##       reimbursement2008 < 11540  to the right, improve=5.6370950, (0 missing)
##       bucket2008        < 2.5    to the right, improve=2.9317810, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.7284423, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.3506867, (0 missing)
## 
## Node number 226: 4266 observations,    complexity param=0.0001051618
##   predicted class=B1  expected loss=0.4946085  P(node) =0.01552385
##     class counts:  2156  1343   503   238    26
##    probabilities: 0.505 0.315 0.118 0.056 0.006 
##   left son=452 (3304 obs) right son=453 (962 obs)
##   Primary splits:
##       osteoporosis      < 0.5    to the left,  improve=10.212680, (0 missing)
##       reimbursement2008 < 5905   to the right, improve= 9.673580, (0 missing)
##       bucket2008        < 2.5    to the right, improve= 7.844764, (0 missing)
##       heart.failure     < 0.5    to the left,  improve= 6.371374, (0 missing)
##       age               < 62.5   to the left,  improve= 3.683231, (0 missing)
## 
## Node number 227: 2224 observations,    complexity param=0.0001217663
##   predicted class=B1  expected loss=0.6007194  P(node) =0.00809307
##     class counts:   888   786   361   169    20
##    probabilities: 0.399 0.353 0.162 0.076 0.009 
##   left son=454 (1518 obs) right son=455 (706 obs)
##   Primary splits:
##       kidney            < 0.5    to the left,  improve=6.746609, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=4.569316, (0 missing)
##       reimbursement2008 < 10710  to the left,  improve=3.711923, (0 missing)
##       age               < 39.5   to the right, improve=3.285727, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=2.661027, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 14380  to the left,  agree=0.714, adj=0.101, (0 split)
##       bucket2008        < 3.5    to the left,  agree=0.708, adj=0.081, (0 split)
##       age               < 98.5   to the left,  agree=0.684, adj=0.004, (0 split)
## 
## Node number 228: 1359 observations,    complexity param=5.313437e-05
##   predicted class=B2  expected loss=0.5548197  P(node) =0.004945361
##     class counts:   467   605   203    76     8
##    probabilities: 0.344 0.445 0.149 0.056 0.006 
##   left son=456 (440 obs) right son=457 (919 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=3.300387, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=2.001264, (0 missing)
##       reimbursement2008 < 3265   to the left,  improve=1.998939, (0 missing)
##       depression        < 0.5    to the left,  improve=1.755319, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.574681, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 3095   to the left,  agree=0.678, adj=0.007, (0 split)
##       age               < 29.5   to the left,  agree=0.678, adj=0.005, (0 split)
## 
## Node number 229: 981 observations
##   predicted class=B2  expected loss=0.5259939  P(node) =0.00356983
##     class counts:   253   465   188    68     7
##    probabilities: 0.258 0.474 0.192 0.069 0.007 
## 
## Node number 230: 714 observations,    complexity param=0.0001881842
##   predicted class=B1  expected loss=0.5546218  P(node) =0.002598225
##     class counts:   318   239    91    61     5
##    probabilities: 0.445 0.335 0.127 0.085 0.007 
##   left son=460 (412 obs) right son=461 (302 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=8.699660, (0 missing)
##       age               < 92.5   to the right, improve=3.253447, (0 missing)
##       reimbursement2008 < 14980  to the left,  improve=2.826720, (0 missing)
##       bucket2008        < 3.5    to the left,  improve=2.191697, (0 missing)
##       kidney            < 0.5    to the left,  improve=2.037790, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 32685  to the left,  agree=0.591, adj=0.033, (0 split)
##       age               < 35.5   to the right, agree=0.583, adj=0.013, (0 split)
## 
## Node number 231: 444 observations,    complexity param=6.088314e-05
##   predicted class=B2  expected loss=0.6126126  P(node) =0.001615703
##     class counts:   118   172   107    38     9
##    probabilities: 0.266 0.387 0.241 0.086 0.020 
##   left son=462 (282 obs) right son=463 (162 obs)
##   Primary splits:
##       osteoporosis      < 0.5    to the left,  improve=3.735228, (0 missing)
##       kidney            < 0.5    to the left,  improve=3.274615, (0 missing)
##       reimbursement2008 < 68975  to the right, improve=3.185223, (0 missing)
##       ihd               < 0.5    to the left,  improve=3.085645, (0 missing)
##       age               < 76.5   to the left,  improve=1.652811, (0 missing)
##   Surrogate splits:
##       age               < 95.5   to the left,  agree=0.644, adj=0.025, (0 split)
##       reimbursement2008 < 8635   to the right, agree=0.637, adj=0.006, (0 split)
## 
## Node number 236: 745 observations,    complexity param=6.167383e-05
##   predicted class=B2  expected loss=0.6228188  P(node) =0.002711033
##     class counts:   232   281   150    72    10
##    probabilities: 0.311 0.377 0.201 0.097 0.013 
##   left son=472 (159 obs) right son=473 (586 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=3.002920, (0 missing)
##       reimbursement2008 < 58135  to the left,  improve=2.259882, (0 missing)
##       depression        < 0.5    to the left,  improve=2.111862, (0 missing)
##       bucket2008        < 4.5    to the left,  improve=1.991400, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.920660, (0 missing)
## 
## Node number 237: 309 observations,    complexity param=6.167383e-05
##   predicted class=B2  expected loss=0.6213592  P(node) =0.001124442
##     class counts:    49   117   100    37     6
##    probabilities: 0.159 0.379 0.324 0.120 0.019 
##   left son=474 (237 obs) right son=475 (72 obs)
##   Primary splits:
##       reimbursement2008 < 10960  to the right, improve=2.966323, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.571780, (0 missing)
##       age               < 90.5   to the left,  improve=1.407411, (0 missing)
##       copd              < 0.5    to the left,  improve=1.306020, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.907593, (0 missing)
## 
## Node number 238: 243 observations
##   predicted class=B2  expected loss=0.526749  P(node) =0.0008842698
##     class counts:    33   115    67    24     4
##    probabilities: 0.136 0.473 0.276 0.099 0.016 
## 
## Node number 239: 297 observations,    complexity param=6.167383e-05
##   predicted class=B3  expected loss=0.6430976  P(node) =0.001080774
##     class counts:    52   104   106    33     2
##    probabilities: 0.175 0.350 0.357 0.111 0.007 
##   left son=478 (226 obs) right son=479 (71 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=1.480103, (0 missing)
##       reimbursement2008 < 6875   to the right, improve=1.383473, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.357727, (0 missing)
##       age               < 54     to the left,  improve=1.263809, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.096200, (0 missing)
## 
## Node number 240: 2617 observations,    complexity param=0.0001439056
##   predicted class=B1  expected loss=0.5257929  P(node) =0.009523186
##     class counts:  1241   884   351   127    14
##    probabilities: 0.474 0.338 0.134 0.049 0.005 
##   left son=480 (403 obs) right son=481 (2214 obs)
##   Primary splits:
##       reimbursement2008 < 9400   to the right, improve=12.428110, (0 missing)
##       bucket2008        < 2.5    to the right, improve= 8.843694, (0 missing)
##       depression        < 0.5    to the left,  improve= 8.588030, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve= 8.405901, (0 missing)
##       alzheimers        < 0.5    to the left,  improve= 4.036896, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.947, adj=0.658, (0 split)
## 
## Node number 241: 9955 observations,    complexity param=0.0009879673
##   predicted class=B2  expected loss=0.6003014  P(node) =0.03622595
##     class counts:  3603  3979  1649   664    60
##    probabilities: 0.362 0.400 0.166 0.067 0.006 
##   left son=482 (5563 obs) right son=483 (4392 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=24.69099, (0 missing)
##       copd              < 0.5    to the left,  improve=17.49244, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=17.05734, (0 missing)
##       reimbursement2008 < 8955   to the left,  improve=14.88623, (0 missing)
##       bucket2008        < 2.5    to the left,  improve= 9.99202, (0 missing)
##   Surrogate splits:
##       alzheimers        < 0.5    to the left,  agree=0.574, adj=0.034, (0 split)
##       age               < 47.5   to the right, agree=0.565, adj=0.013, (0 split)
##       copd              < 0.5    to the left,  agree=0.564, adj=0.013, (0 split)
##       reimbursement2008 < 13565  to the left,  agree=0.561, adj=0.005, (0 split)
##       bucket2008        < 3.5    to the left,  agree=0.559, adj=0.001, (0 split)
## 
## Node number 244: 4305 observations,    complexity param=6.918538e-05
##   predicted class=B2  expected loss=0.524971  P(node) =0.01566577
##     class counts:  1149  2045   746   328    37
##    probabilities: 0.267 0.475 0.173 0.076 0.009 
##   left son=488 (1063 obs) right son=489 (3242 obs)
##   Primary splits:
##       reimbursement2008 < 9880   to the right, improve=11.346300, (0 missing)
##       bucket2008        < 2.5    to the right, improve= 8.562449, (0 missing)
##       ihd               < 0.5    to the left,  improve= 7.353611, (0 missing)
##       copd              < 0.5    to the left,  improve= 6.701463, (0 missing)
##       heart.failure     < 0.5    to the left,  improve= 3.881008, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.941, adj=0.762, (0 split)
## 
## Node number 245: 829 observations
##   predicted class=B2  expected loss=0.4885404  P(node) =0.003016707
##     class counts:   128   424   190    84     3
##    probabilities: 0.154 0.511 0.229 0.101 0.004 
## 
## Node number 248: 7786 observations,    complexity param=0.0001411382
##   predicted class=B2  expected loss=0.6050604  P(node) =0.02833302
##     class counts:  1982  3075  1667   929   133
##    probabilities: 0.255 0.395 0.214 0.119 0.017 
##   left son=496 (964 obs) right son=497 (6822 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=18.914850, (0 missing)
##       depression        < 0.5    to the left,  improve=16.457650, (0 missing)
##       reimbursement2008 < 6325   to the left,  improve=12.927220, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve= 9.344273, (0 missing)
##       bucket2008        < 2.5    to the left,  improve= 9.314433, (0 missing)
## 
## Node number 249: 1638 observations
##   predicted class=B2  expected loss=0.5714286  P(node) =0.005960634
##     class counts:   210   702   472   227    27
##    probabilities: 0.128 0.429 0.288 0.139 0.016 
## 
## Node number 252: 3345 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.580568  P(node) =0.01217236
##     class counts:   372  1403   837   632   101
##    probabilities: 0.111 0.419 0.250 0.189 0.030 
##   left son=504 (1291 obs) right son=505 (2054 obs)
##   Primary splits:
##       depression    < 0.5    to the left,  improve=6.733363, (0 missing)
##       copd          < 0.5    to the left,  improve=6.399894, (0 missing)
##       cancer        < 0.5    to the left,  improve=5.398776, (0 missing)
##       heart.failure < 0.5    to the left,  improve=3.401421, (0 missing)
##       age           < 31.5   to the right, improve=3.041832, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 15665  to the left,  agree=0.614, adj=0.001, (0 split)
## 
## Node number 253: 2057 observations,    complexity param=7.748763e-05
##   predicted class=B2  expected loss=0.6212931  P(node) =0.007485362
##     class counts:   137   779   473   554   114
##    probabilities: 0.067 0.379 0.230 0.269 0.055 
##   left son=506 (520 obs) right son=507 (1537 obs)
##   Primary splits:
##       copd          < 0.5    to the left,  improve=4.741452, (0 missing)
##       age           < 62.5   to the right, improve=3.709690, (0 missing)
##       cancer        < 0.5    to the right, improve=3.631891, (0 missing)
##       ihd           < 0.5    to the left,  improve=3.269099, (0 missing)
##       heart.failure < 0.5    to the left,  improve=3.168350, (0 missing)
##   Surrogate splits:
##       heart.failure < 0.5    to the left,  agree=0.751, adj=0.015, (0 split)
##       age           < 29     to the left,  agree=0.749, adj=0.008, (0 split)
##       ihd           < 0.5    to the left,  agree=0.749, adj=0.008, (0 split)
## 
## Node number 254: 5298 observations,    complexity param=0.0002036818
##   predicted class=B2  expected loss=0.689128  P(node) =0.01927927
##     class counts:   908  1647  1051  1364   328
##    probabilities: 0.171 0.311 0.198 0.257 0.062 
##   left son=508 (2489 obs) right son=509 (2809 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=18.17296, (0 missing)
##       reimbursement2008 < 22335  to the left,  improve=13.06444, (0 missing)
##       copd              < 0.5    to the left,  improve=11.53148, (0 missing)
##       ihd               < 0.5    to the left,  improve= 8.63716, (0 missing)
##       heart.failure     < 0.5    to the left,  improve= 8.42218, (0 missing)
##   Surrogate splits:
##       copd              < 0.5    to the left,  agree=0.579, adj=0.104, (0 split)
##       alzheimers        < 0.5    to the left,  agree=0.573, adj=0.092, (0 split)
##       ihd               < 0.5    to the left,  agree=0.545, adj=0.033, (0 split)
##       heart.failure     < 0.5    to the left,  agree=0.544, adj=0.030, (0 split)
##       reimbursement2008 < 16955  to the left,  agree=0.535, adj=0.010, (0 split)
## 
## Node number 255: 1785 observations
##   predicted class=B2  expected loss=0.6369748  P(node) =0.006495562
##     class counts:   174   648   492   397    74
##    probabilities: 0.097 0.363 0.276 0.222 0.041 
## 
## Node number 406: 128 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5  P(node) =0.0004657882
##     class counts:    64    42    14     8     0
##    probabilities: 0.500 0.328 0.109 0.062 0.000 
##   left son=812 (95 obs) right son=813 (33 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=3.3313600, (0 missing)
##       reimbursement2008 < 2155   to the left,  improve=2.1875000, (0 missing)
##       age               < 70.5   to the right, improve=1.5228130, (0 missing)
##       arthritis         < 0.5    to the left,  improve=1.1806970, (0 missing)
##       copd              < 0.5    to the left,  improve=0.4207762, (0 missing)
## 
## Node number 407: 158 observations
##   predicted class=B2  expected loss=0.5379747  P(node) =0.0005749573
##     class counts:    55    73    22     8     0
##    probabilities: 0.348 0.462 0.139 0.051 0.000 
## 
## Node number 412: 270 observations
##   predicted class=B1  expected loss=0.462963  P(node) =0.000982522
##     class counts:   145    73    36    15     1
##    probabilities: 0.537 0.270 0.133 0.056 0.004 
## 
## Node number 413: 634 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5694006  P(node) =0.002307107
##     class counts:   273   231    83    42     5
##    probabilities: 0.431 0.364 0.131 0.066 0.008 
##   left son=826 (596 obs) right son=827 (38 obs)
##   Primary splits:
##       age               < 91.5   to the left,  improve=3.6059530, (0 missing)
##       ihd               < 0.5    to the left,  improve=2.2411130, (0 missing)
##       reimbursement2008 < 1765   to the right, improve=2.0115470, (0 missing)
##       cancer            < 0.5    to the left,  improve=1.8824720, (0 missing)
##       depression        < 0.5    to the right, improve=0.5863526, (0 missing)
## 
## Node number 422: 763 observations
##   predicted class=B1  expected loss=0.5307995  P(node) =0.002776534
##     class counts:   358   260   102    40     3
##    probabilities: 0.469 0.341 0.134 0.052 0.004 
## 
## Node number 423: 118 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5338983  P(node) =0.0004293985
##     class counts:    41    55    16     6     0
##    probabilities: 0.347 0.466 0.136 0.051 0.000 
##   left son=846 (22 obs) right son=847 (96 obs)
##   Primary splits:
##       reimbursement2008 < 2865   to the right, improve=2.6611130, (0 missing)
##       copd              < 0.5    to the left,  improve=1.5528850, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.3108310, (0 missing)
##       bucket2008        < 1.5    to the left,  improve=1.2553930, (0 missing)
##       age               < 89.5   to the left,  improve=0.9696791, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the right, agree=0.873, adj=0.318, (0 split)
## 
## Node number 424: 495 observations,    complexity param=8.855729e-05
##   predicted class=B1  expected loss=0.5656566  P(node) =0.00180129
##     class counts:   215   202    50    26     2
##    probabilities: 0.434 0.408 0.101 0.053 0.004 
##   left son=848 (385 obs) right son=849 (110 obs)
##   Primary splits:
##       reimbursement2008 < 2795   to the right, improve=2.2427130, (0 missing)
##       age               < 73.5   to the left,  improve=1.5903260, (0 missing)
##       ihd               < 0.5    to the left,  improve=1.1717170, (0 missing)
##       depression        < 0.5    to the left,  improve=0.5724615, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.5572971, (0 missing)
## 
## Node number 425: 943 observations
##   predicted class=B1  expected loss=0.5344645  P(node) =0.003431549
##     class counts:   439   313   137    50     4
##    probabilities: 0.466 0.332 0.145 0.053 0.004 
## 
## Node number 432: 1265 observations
##   predicted class=B1  expected loss=0.4442688  P(node) =0.004603298
##     class counts:   703   367   147    44     4
##    probabilities: 0.556 0.290 0.116 0.035 0.003 
## 
## Node number 433: 2727 observations,    complexity param=6.918538e-05
##   predicted class=B1  expected loss=0.5192519  P(node) =0.009923472
##     class counts:  1311   948   350   109     9
##    probabilities: 0.481 0.348 0.128 0.040 0.003 
##   left son=866 (1499 obs) right son=867 (1228 obs)
##   Primary splits:
##       reimbursement2008 < 2615   to the left,  improve=4.028460, (0 missing)
##       age               < 54.5   to the left,  improve=3.426946, (0 missing)
##       copd              < 0.5    to the left,  improve=2.215284, (0 missing)
##       stroke            < 0.5    to the left,  improve=2.121876, (0 missing)
##       depression        < 0.5    to the left,  improve=1.918448, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the left,  agree=0.612, adj=0.139, (0 split)
##       age        < 97.5   to the left,  agree=0.552, adj=0.005, (0 split)
## 
## Node number 434: 238 observations,    complexity param=0.0001826494
##   predicted class=B1  expected loss=0.5714286  P(node) =0.000866075
##     class counts:   102    86    38     9     3
##    probabilities: 0.429 0.361 0.160 0.038 0.013 
##   left son=868 (167 obs) right son=869 (71 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=4.834875, (0 missing)
##       age               < 59.5   to the right, improve=2.461134, (0 missing)
##       ihd               < 0.5    to the left,  improve=1.909944, (0 missing)
##       reimbursement2008 < 2285   to the left,  improve=1.842456, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.113912, (0 missing)
##   Surrogate splits:
##       age < 97.5   to the left,  agree=0.718, adj=0.056, (0 split)
## 
## Node number 435: 145 observations
##   predicted class=B2  expected loss=0.4689655  P(node) =0.0005276507
##     class counts:    39    77    20     8     1
##    probabilities: 0.269 0.531 0.138 0.055 0.007 
## 
## Node number 436: 635 observations
##   predicted class=B1  expected loss=0.5023622  P(node) =0.002310746
##     class counts:   316   196    93    26     4
##    probabilities: 0.498 0.309 0.146 0.041 0.006 
## 
## Node number 437: 2184 observations,    complexity param=0.0001129105
##   predicted class=B1  expected loss=0.595696  P(node) =0.007947511
##     class counts:   883   784   346   157    14
##    probabilities: 0.404 0.359 0.158 0.072 0.006 
##   left son=874 (393 obs) right son=875 (1791 obs)
##   Primary splits:
##       reimbursement2008 < 2315   to the left,  improve=4.386891, (0 missing)
##       depression        < 0.5    to the left,  improve=4.376862, (0 missing)
##       age               < 39.5   to the right, improve=3.004733, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=2.391734, (0 missing)
##       stroke            < 0.5    to the left,  improve=2.171601, (0 missing)
## 
## Node number 438: 613 observations,    complexity param=8.302246e-05
##   predicted class=B1  expected loss=0.6182708  P(node) =0.002230689
##     class counts:   234   226   111    36     6
##    probabilities: 0.382 0.369 0.181 0.059 0.010 
##   left son=876 (180 obs) right son=877 (433 obs)
##   Primary splits:
##       osteoporosis      < 0.5    to the right, improve=1.4494640, (0 missing)
##       age               < 98.5   to the right, improve=1.3979840, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.9190213, (0 missing)
##       reimbursement2008 < 2275   to the left,  improve=0.8284921, (0 missing)
##       depression        < 0.5    to the left,  improve=0.7804891, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 2605   to the right, agree=0.713, adj=0.022, (0 split)
##       age               < 99.5   to the right, agree=0.711, adj=0.017, (0 split)
## 
## Node number 439: 606 observations
##   predicted class=B2  expected loss=0.5726073  P(node) =0.002205216
##     class counts:   172   259   120    49     6
##    probabilities: 0.284 0.427 0.198 0.081 0.010 
## 
## Node number 440: 143 observations
##   predicted class=B1  expected loss=0.3986014  P(node) =0.0005203728
##     class counts:    86    37    11     9     0
##    probabilities: 0.601 0.259 0.077 0.063 0.000 
## 
## Node number 441: 374 observations,    complexity param=7.19528e-05
##   predicted class=B1  expected loss=0.5802139  P(node) =0.001360975
##     class counts:   157   154    46    16     1
##    probabilities: 0.420 0.412 0.123 0.043 0.003 
##   left son=882 (25 obs) right son=883 (349 obs)
##   Primary splits:
##       reimbursement2008 < 2315   to the left,  improve=4.569334, (0 missing)
##       cancer            < 0.5    to the left,  improve=2.355946, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.361181, (0 missing)
##       age               < 90.5   to the right, improve=1.103565, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.082873, (0 missing)
## 
## Node number 442: 18 observations
##   predicted class=B1  expected loss=0.2777778  P(node) =6.550147e-05
##     class counts:    13     4     0     1     0
##    probabilities: 0.722 0.222 0.000 0.056 0.000 
## 
## Node number 443: 1533 observations,    complexity param=7.748763e-05
##   predicted class=B2  expected loss=0.5942596  P(node) =0.005578542
##     class counts:   588   622   223    91     9
##    probabilities: 0.384 0.406 0.145 0.059 0.006 
##   left son=886 (1101 obs) right son=887 (432 obs)
##   Primary splits:
##       kidney            < 0.5    to the left,  improve=2.2490350, (0 missing)
##       cancer            < 0.5    to the left,  improve=1.4724050, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.3260620, (0 missing)
##       reimbursement2008 < 2435   to the left,  improve=1.1404580, (0 missing)
##       copd              < 0.5    to the left,  improve=0.9660973, (0 missing)
## 
## Node number 450: 810 observations,    complexity param=5.258089e-05
##   predicted class=B1  expected loss=0.491358  P(node) =0.002947566
##     class counts:   412   257   108    33     0
##    probabilities: 0.509 0.317 0.133 0.041 0.000 
##   left son=900 (117 obs) right son=901 (693 obs)
##   Primary splits:
##       reimbursement2008 < 11525  to the right, improve=5.1504220, (0 missing)
##       bucket2008        < 2.5    to the right, improve=2.8801500, (0 missing)
##       age               < 34.5   to the left,  improve=1.2970260, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.8677994, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.5279869, (0 missing)
##   Surrogate splits:
##       bucket2008 < 3.5    to the right, agree=0.928, adj=0.504, (0 split)
## 
## Node number 451: 33 observations
##   predicted class=B2  expected loss=0.4242424  P(node) =0.000120086
##     class counts:     6    19     7     1     0
##    probabilities: 0.182 0.576 0.212 0.030 0.000 
## 
## Node number 452: 3304 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.4757869  P(node) =0.01202316
##     class counts:  1732   979   389   183    21
##    probabilities: 0.524 0.296 0.118 0.055 0.006 
##   left son=904 (1626 obs) right son=905 (1678 obs)
##   Primary splits:
##       reimbursement2008 < 5905   to the right, improve=9.971499, (0 missing)
##       bucket2008        < 2.5    to the right, improve=7.851328, (0 missing)
##       age               < 62.5   to the left,  improve=4.441278, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.580749, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.765354, (0 missing)
##   Surrogate splits:
##       bucket2008    < 2.5    to the right, agree=0.861, adj=0.717, (0 split)
##       kidney        < 0.5    to the right, agree=0.637, adj=0.262, (0 split)
##       heart.failure < 0.5    to the right, agree=0.608, adj=0.204, (0 split)
##       copd          < 0.5    to the right, agree=0.590, adj=0.166, (0 split)
##       alzheimers    < 0.5    to the right, agree=0.558, adj=0.102, (0 split)
## 
## Node number 453: 962 observations,    complexity param=0.0001051618
##   predicted class=B1  expected loss=0.5592516  P(node) =0.00350069
##     class counts:   424   364   114    55     5
##    probabilities: 0.441 0.378 0.119 0.057 0.005 
##   left son=906 (857 obs) right son=907 (105 obs)
##   Primary splits:
##       stroke            < 0.5    to the left,  improve=3.576264, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.114700, (0 missing)
##       reimbursement2008 < 59635  to the left,  improve=2.145451, (0 missing)
##       age               < 97.5   to the right, improve=1.742305, (0 missing)
##       copd              < 0.5    to the left,  improve=1.012750, (0 missing)
## 
## Node number 454: 1518 observations
##   predicted class=B1  expected loss=0.5685112  P(node) =0.005523957
##     class counts:   655   520   243    92     8
##    probabilities: 0.431 0.343 0.160 0.061 0.005 
## 
## Node number 455: 706 observations,    complexity param=0.0001162314
##   predicted class=B2  expected loss=0.6232295  P(node) =0.002569113
##     class counts:   233   266   118    77    12
##    probabilities: 0.330 0.377 0.167 0.109 0.017 
##   left son=910 (696 obs) right son=911 (10 obs)
##   Primary splits:
##       reimbursement2008 < 3155   to the right, improve=3.301177, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.232296, (0 missing)
##       copd              < 0.5    to the left,  improve=2.330270, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.835216, (0 missing)
##       age               < 92.5   to the right, improve=1.805094, (0 missing)
## 
## Node number 456: 440 observations,    complexity param=5.313437e-05
##   predicted class=B2  expected loss=0.5636364  P(node) =0.001601147
##     class counts:   177   192    49    20     2
##    probabilities: 0.402 0.436 0.111 0.045 0.005 
##   left son=912 (58 obs) right son=913 (382 obs)
##   Primary splits:
##       reimbursement2008 < 3155   to the left,  improve=3.3827400, (0 missing)
##       age               < 80.5   to the right, improve=2.1481460, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.6300393, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.5745512, (0 missing)
##       depression        < 0.5    to the right, improve=0.5547491, (0 missing)
## 
## Node number 457: 919 observations
##   predicted class=B2  expected loss=0.5505985  P(node) =0.003344214
##     class counts:   290   413   154    56     6
##    probabilities: 0.316 0.449 0.168 0.061 0.007 
## 
## Node number 460: 412 observations
##   predicted class=B1  expected loss=0.4757282  P(node) =0.001499256
##     class counts:   216   120    42    30     4
##    probabilities: 0.524 0.291 0.102 0.073 0.010 
## 
## Node number 461: 302 observations,    complexity param=7.748763e-05
##   predicted class=B2  expected loss=0.6059603  P(node) =0.001098969
##     class counts:   102   119    49    31     1
##    probabilities: 0.338 0.394 0.162 0.103 0.003 
##   left son=922 (9 obs) right son=923 (293 obs)
##   Primary splits:
##       age               < 92.5   to the right, improve=2.5766490, (0 missing)
##       stroke            < 0.5    to the right, improve=1.8961920, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.0608030, (0 missing)
##       reimbursement2008 < 32980  to the right, improve=1.0319510, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.7951977, (0 missing)
## 
## Node number 462: 282 observations,    complexity param=6.088314e-05
##   predicted class=B2  expected loss=0.6631206  P(node) =0.00102619
##     class counts:    88    95    71    23     5
##    probabilities: 0.312 0.337 0.252 0.082 0.018 
##   left son=924 (220 obs) right son=925 (62 obs)
##   Primary splits:
##       reimbursement2008 < 27390  to the left,  improve=2.933452, (0 missing)
##       age               < 79.5   to the left,  improve=2.171675, (0 missing)
##       bucket2008        < 4.5    to the right, improve=1.933271, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.142914, (0 missing)
##       ihd               < 0.5    to the left,  improve=1.125301, (0 missing)
##   Surrogate splits:
##       bucket2008 < 3.5    to the left,  agree=0.816, adj=0.161, (0 split)
## 
## Node number 463: 162 observations
##   predicted class=B2  expected loss=0.5246914  P(node) =0.0005895132
##     class counts:    30    77    36    15     4
##    probabilities: 0.185 0.475 0.222 0.093 0.025 
## 
## Node number 472: 159 observations,    complexity param=6.167383e-05
##   predicted class=B1  expected loss=0.591195  P(node) =0.0005785963
##     class counts:    65    51    33     8     2
##    probabilities: 0.409 0.321 0.208 0.050 0.013 
##   left son=944 (76 obs) right son=945 (83 obs)
##   Primary splits:
##       reimbursement2008 < 11995  to the right, improve=3.4294220, (0 missing)
##       age               < 65     to the left,  improve=1.4674530, (0 missing)
##       copd              < 0.5    to the left,  improve=1.0021090, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.7722947, (0 missing)
##       bucket2008        < 3.5    to the left,  improve=0.4091195, (0 missing)
##   Surrogate splits:
##       bucket2008    < 3.5    to the right, agree=0.673, adj=0.316, (0 split)
##       alzheimers    < 0.5    to the right, agree=0.591, adj=0.145, (0 split)
##       copd          < 0.5    to the right, agree=0.572, adj=0.105, (0 split)
##       heart.failure < 0.5    to the right, agree=0.560, adj=0.079, (0 split)
##       age           < 85.5   to the right, agree=0.541, adj=0.039, (0 split)
## 
## Node number 473: 586 observations
##   predicted class=B2  expected loss=0.6075085  P(node) =0.002132437
##     class counts:   167   230   117    64     8
##    probabilities: 0.285 0.392 0.200 0.109 0.014 
## 
## Node number 474: 237 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.5738397  P(node) =0.000862436
##     class counts:    35   101    72    26     3
##    probabilities: 0.148 0.426 0.304 0.110 0.013 
##   left son=948 (126 obs) right son=949 (111 obs)
##   Primary splits:
##       copd              < 0.5    to the left,  improve=2.2728500, (0 missing)
##       reimbursement2008 < 18275  to the left,  improve=1.9530690, (0 missing)
##       bucket2008        < 3.5    to the left,  improve=1.1622440, (0 missing)
##       age               < 75.5   to the right, improve=1.0471110, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.8993424, (0 missing)
##   Surrogate splits:
##       age           < 86.5   to the left,  agree=0.599, adj=0.144, (0 split)
##       heart.failure < 0.5    to the left,  agree=0.586, adj=0.117, (0 split)
##       kidney        < 0.5    to the left,  agree=0.582, adj=0.108, (0 split)
##       depression    < 0.5    to the left,  agree=0.570, adj=0.081, (0 split)
##       stroke        < 0.5    to the left,  agree=0.565, adj=0.072, (0 split)
## 
## Node number 475: 72 observations
##   predicted class=B3  expected loss=0.6111111  P(node) =0.0002620059
##     class counts:    14    16    28    11     3
##    probabilities: 0.194 0.222 0.389 0.153 0.042 
## 
## Node number 478: 226 observations
##   predicted class=B2  expected loss=0.6238938  P(node) =0.0008224073
##     class counts:    40    85    74    26     1
##    probabilities: 0.177 0.376 0.327 0.115 0.004 
## 
## Node number 479: 71 observations
##   predicted class=B3  expected loss=0.5492958  P(node) =0.0002583669
##     class counts:    12    19    32     7     1
##    probabilities: 0.169 0.268 0.451 0.099 0.014 
## 
## Node number 480: 403 observations
##   predicted class=B1  expected loss=0.4243176  P(node) =0.001466505
##     class counts:   232    86    61    20     4
##    probabilities: 0.576 0.213 0.151 0.050 0.010 
## 
## Node number 481: 2214 observations,    complexity param=0.0001439056
##   predicted class=B1  expected loss=0.5442638  P(node) =0.008056681
##     class counts:  1009   798   290   107    10
##    probabilities: 0.456 0.360 0.131 0.048 0.005 
##   left son=962 (1636 obs) right son=963 (578 obs)
##   Primary splits:
##       osteoporosis < 0.5    to the left,  improve=6.829533, (0 missing)
##       depression   < 0.5    to the left,  improve=5.940363, (0 missing)
##       copd         < 0.5    to the left,  improve=3.544519, (0 missing)
##       alzheimers   < 0.5    to the left,  improve=2.874488, (0 missing)
##       age          < 57.5   to the left,  improve=2.182371, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 9185   to the left,  agree=0.742, adj=0.01, (0 split)
## 
## Node number 482: 5563 observations,    complexity param=0.000785946
##   predicted class=B1  expected loss=0.6002157  P(node) =0.02024359
##     class counts:  2224  2125   853   328    33
##    probabilities: 0.400 0.382 0.153 0.059 0.006 
##   left son=964 (1363 obs) right son=965 (4200 obs)
##   Primary splits:
##       reimbursement2008 < 8955   to the right, improve=10.881900, (0 missing)
##       heart.failure     < 0.5    to the left,  improve= 9.391652, (0 missing)
##       copd              < 0.5    to the left,  improve= 8.088751, (0 missing)
##       bucket2008        < 2.5    to the right, improve= 7.479912, (0 missing)
##       age               < 31.5   to the left,  improve= 2.090877, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.959, adj=0.834, (0 split)
##       age        < 28.5   to the left,  agree=0.755, adj=0.001, (0 split)
## 
## Node number 483: 4392 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.5778689  P(node) =0.01598236
##     class counts:  1379  1854   796   336    27
##    probabilities: 0.314 0.422 0.181 0.077 0.006 
##   left son=966 (2928 obs) right son=967 (1464 obs)
##   Primary splits:
##       reimbursement2008 < 8325   to the left,  improve=7.509791, (0 missing)
##       copd              < 0.5    to the left,  improve=6.714633, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=5.287260, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=5.126640, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=3.940785, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.979, adj=0.936, (0 split)
## 
## Node number 488: 1063 observations,    complexity param=6.918538e-05
##   predicted class=B2  expected loss=0.5983067  P(node) =0.003868226
##     class counts:   336   427   192    95    13
##    probabilities: 0.316 0.402 0.181 0.089 0.012 
##   left son=976 (102 obs) right son=977 (961 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=6.866573, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=4.371067, (0 missing)
##       copd              < 0.5    to the left,  improve=3.836869, (0 missing)
##       reimbursement2008 < 18130  to the left,  improve=3.632764, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=2.933358, (0 missing)
## 
## Node number 489: 3242 observations
##   predicted class=B2  expected loss=0.5009254  P(node) =0.01179754
##     class counts:   813  1618   554   233    24
##    probabilities: 0.251 0.499 0.171 0.072 0.007 
## 
## Node number 496: 964 observations,    complexity param=0.0001411382
##   predicted class=B1  expected loss=0.6307054  P(node) =0.003507968
##     class counts:   356   348   167    82    11
##    probabilities: 0.369 0.361 0.173 0.085 0.011 
##   left son=992 (572 obs) right son=993 (392 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=5.116701, (0 missing)
##       reimbursement2008 < 8255   to the left,  improve=4.417859, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=3.940989, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.162605, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=3.038248, (0 missing)
##   Surrogate splits:
##       stroke            < 0.5    to the left,  agree=0.601, adj=0.018, (0 split)
##       reimbursement2008 < 14855  to the left,  agree=0.598, adj=0.010, (0 split)
##       copd              < 0.5    to the left,  agree=0.594, adj=0.003, (0 split)
## 
## Node number 497: 6822 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.6002639  P(node) =0.02482506
##     class counts:  1626  2727  1500   847   122
##    probabilities: 0.238 0.400 0.220 0.124 0.018 
##   left son=994 (3172 obs) right son=995 (3650 obs)
##   Primary splits:
##       reimbursement2008 < 6325   to the left,  improve=11.481700, (0 missing)
##       depression        < 0.5    to the left,  improve=11.166950, (0 missing)
##       bucket2008        < 2.5    to the left,  improve= 7.602059, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve= 6.404955, (0 missing)
##       copd              < 0.5    to the left,  improve= 5.945024, (0 missing)
##   Surrogate splits:
##       bucket2008    < 2.5    to the left,  agree=0.869, adj=0.717, (0 split)
##       copd          < 0.5    to the left,  agree=0.576, adj=0.088, (0 split)
##       heart.failure < 0.5    to the left,  agree=0.570, adj=0.076, (0 split)
##       alzheimers    < 0.5    to the left,  agree=0.545, adj=0.020, (0 split)
##       age           < 31.5   to the left,  agree=0.536, adj=0.003, (0 split)
## 
## Node number 504: 1291 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5530596  P(node) =0.004697911
##     class counts:   183   577   280   219    32
##    probabilities: 0.142 0.447 0.217 0.170 0.025 
##   left son=1008 (973 obs) right son=1009 (318 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=5.737334, (0 missing)
##       reimbursement2008 < 32055  to the left,  improve=1.892346, (0 missing)
##       age               < 84.5   to the right, improve=1.763761, (0 missing)
##       ihd               < 0.5    to the left,  improve=1.710826, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.100420, (0 missing)
##   Surrogate splits:
##       age < 28.5   to the right, agree=0.755, adj=0.006, (0 split)
## 
## Node number 505: 2054 observations
##   predicted class=B2  expected loss=0.5978578  P(node) =0.007474445
##     class counts:   189   826   557   413    69
##    probabilities: 0.092 0.402 0.271 0.201 0.034 
## 
## Node number 506: 520 observations
##   predicted class=B2  expected loss=0.5769231  P(node) =0.001892265
##     class counts:    50   220   120   108    22
##    probabilities: 0.096 0.423 0.231 0.208 0.042 
## 
## Node number 507: 1537 observations,    complexity param=7.748763e-05
##   predicted class=B2  expected loss=0.6363045  P(node) =0.005593098
##     class counts:    87   559   353   446    92
##    probabilities: 0.057 0.364 0.230 0.290 0.060 
##   left son=1014 (1286 obs) right son=1015 (251 obs)
##   Primary splits:
##       age               < 62.5   to the right, improve=4.292573, (0 missing)
##       reimbursement2008 < 43950  to the left,  improve=3.358938, (0 missing)
##       cancer            < 0.5    to the right, improve=2.803709, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.956332, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.605851, (0 missing)
## 
## Node number 508: 2489 observations,    complexity param=0.0002036818
##   predicted class=B2  expected loss=0.7219767  P(node) =0.009057397
##     class counts:   541   692   436   670   150
##    probabilities: 0.217 0.278 0.175 0.269 0.060 
##   left son=1016 (1317 obs) right son=1017 (1172 obs)
##   Primary splits:
##       copd              < 0.5    to the right, improve=9.293163, (0 missing)
##       reimbursement2008 < 23175  to the left,  improve=7.265866, (0 missing)
##       ihd               < 0.5    to the left,  improve=7.177016, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=4.187307, (0 missing)
##       bucket2008        < 4.5    to the left,  improve=3.684093, (0 missing)
##   Surrogate splits:
##       heart.failure     < 0.5    to the right, agree=0.575, adj=0.097, (0 split)
##       alzheimers        < 0.5    to the right, agree=0.556, adj=0.057, (0 split)
##       ihd               < 0.5    to the right, agree=0.555, adj=0.055, (0 split)
##       reimbursement2008 < 27380  to the right, agree=0.544, adj=0.032, (0 split)
##       age               < 52.5   to the right, agree=0.532, adj=0.005, (0 split)
## 
## Node number 509: 2809 observations
##   predicted class=B2  expected loss=0.6600214  P(node) =0.01022187
##     class counts:   367   955   615   694   178
##    probabilities: 0.131 0.340 0.219 0.247 0.063 
## 
## Node number 812: 95 observations
##   predicted class=B1  expected loss=0.4315789  P(node) =0.0003457022
##     class counts:    54    25    11     5     0
##    probabilities: 0.568 0.263 0.116 0.053 0.000 
## 
## Node number 813: 33 observations
##   predicted class=B2  expected loss=0.4848485  P(node) =0.000120086
##     class counts:    10    17     3     3     0
##    probabilities: 0.303 0.515 0.091 0.091 0.000 
## 
## Node number 826: 596 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5536913  P(node) =0.002168826
##     class counts:   266   214    77    34     5
##    probabilities: 0.446 0.359 0.129 0.057 0.008 
##   left son=1652 (555 obs) right son=1653 (41 obs)
##   Primary splits:
##       reimbursement2008 < 1765   to the right, improve=2.4482060, (0 missing)
##       age               < 81.5   to the right, improve=2.3548750, (0 missing)
##       ihd               < 0.5    to the left,  improve=2.3213280, (0 missing)
##       cancer            < 0.5    to the left,  improve=2.1512770, (0 missing)
##       depression        < 0.5    to the left,  improve=0.5755867, (0 missing)
## 
## Node number 827: 38 observations
##   predicted class=B2  expected loss=0.5526316  P(node) =0.0001382809
##     class counts:     7    17     6     8     0
##    probabilities: 0.184 0.447 0.158 0.211 0.000 
## 
## Node number 846: 22 observations
##   predicted class=B1  expected loss=0.4545455  P(node) =8.005735e-05
##     class counts:    12     5     4     1     0
##    probabilities: 0.545 0.227 0.182 0.045 0.000 
## 
## Node number 847: 96 observations
##   predicted class=B2  expected loss=0.4791667  P(node) =0.0003493412
##     class counts:    29    50    12     5     0
##    probabilities: 0.302 0.521 0.125 0.052 0.000 
## 
## Node number 848: 385 observations,    complexity param=8.855729e-05
##   predicted class=B1  expected loss=0.5454545  P(node) =0.001401004
##     class counts:   175   146    39    23     2
##    probabilities: 0.455 0.379 0.101 0.060 0.005 
##   left son=1696 (263 obs) right son=1697 (122 obs)
##   Primary splits:
##       age               < 80.5   to the left,  improve=2.5496070, (0 missing)
##       ihd               < 0.5    to the left,  improve=1.7465940, (0 missing)
##       reimbursement2008 < 3025   to the right, improve=0.9395484, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.7038989, (0 missing)
##       depression        < 0.5    to the left,  improve=0.3133237, (0 missing)
## 
## Node number 849: 110 observations
##   predicted class=B2  expected loss=0.4909091  P(node) =0.0004002868
##     class counts:    40    56    11     3     0
##    probabilities: 0.364 0.509 0.100 0.027 0.000 
## 
## Node number 866: 1499 observations
##   predicted class=B1  expected loss=0.490994  P(node) =0.005454817
##     class counts:   763   492   189    52     3
##    probabilities: 0.509 0.328 0.126 0.035 0.002 
## 
## Node number 867: 1228 observations,    complexity param=6.918538e-05
##   predicted class=B1  expected loss=0.5537459  P(node) =0.004468656
##     class counts:   548   456   161    57     6
##    probabilities: 0.446 0.371 0.131 0.046 0.005 
##   left son=1734 (171 obs) right son=1735 (1057 obs)
##   Primary splits:
##       reimbursement2008 < 2995   to the right, improve=3.399761, (0 missing)
##       bucket2008        < 1.5    to the right, improve=3.399761, (0 missing)
##       age               < 83.5   to the left,  improve=2.697325, (0 missing)
##       kidney            < 0.5    to the left,  improve=2.465389, (0 missing)
##       depression        < 0.5    to the left,  improve=1.722272, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the right, agree=1, adj=1, (0 split)
## 
## Node number 868: 167 observations
##   predicted class=B1  expected loss=0.502994  P(node) =0.0006077081
##     class counts:    83    50    25     7     2
##    probabilities: 0.497 0.299 0.150 0.042 0.012 
## 
## Node number 869: 71 observations
##   predicted class=B2  expected loss=0.4929577  P(node) =0.0002583669
##     class counts:    19    36    13     2     1
##    probabilities: 0.268 0.507 0.183 0.028 0.014 
## 
## Node number 874: 393 observations
##   predicted class=B1  expected loss=0.5139949  P(node) =0.001430115
##     class counts:   191   134    46    20     2
##    probabilities: 0.486 0.341 0.117 0.051 0.005 
## 
## Node number 875: 1791 observations,    complexity param=0.0001129105
##   predicted class=B1  expected loss=0.6136237  P(node) =0.006517396
##     class counts:   692   650   300   137    12
##    probabilities: 0.386 0.363 0.168 0.076 0.007 
##   left son=1750 (1752 obs) right son=1751 (39 obs)
##   Primary splits:
##       age               < 39.5   to the right, improve=3.631907, (0 missing)
##       depression        < 0.5    to the left,  improve=3.598994, (0 missing)
##       reimbursement2008 < 2475   to the left,  improve=1.828499, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.790378, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.609073, (0 missing)
## 
## Node number 876: 180 observations,    complexity param=8.302246e-05
##   predicted class=B1  expected loss=0.5666667  P(node) =0.0006550147
##     class counts:    78    68    23    11     0
##    probabilities: 0.433 0.378 0.128 0.061 0.000 
##   left son=1752 (112 obs) right son=1753 (68 obs)
##   Primary splits:
##       reimbursement2008 < 2455   to the left,  improve=3.4787820, (0 missing)
##       age               < 88.5   to the left,  improve=1.3486290, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.8074074, (0 missing)
##       copd              < 0.5    to the left,  improve=0.6952328, (0 missing)
##       ihd               < 0.5    to the right, improve=0.3305556, (0 missing)
## 
## Node number 877: 433 observations,    complexity param=6.272808e-05
##   predicted class=B2  expected loss=0.6351039  P(node) =0.001575674
##     class counts:   156   158    88    25     6
##    probabilities: 0.360 0.365 0.203 0.058 0.014 
##   left son=1754 (403 obs) right son=1755 (30 obs)
##   Primary splits:
##       stroke            < 0.5    to the left,  improve=1.8988510, (0 missing)
##       age               < 45.5   to the left,  improve=1.3010460, (0 missing)
##       reimbursement2008 < 2255   to the left,  improve=1.2799960, (0 missing)
##       depression        < 0.5    to the left,  improve=1.2338070, (0 missing)
##       cancer            < 0.5    to the right, improve=0.6525541, (0 missing)
## 
## Node number 882: 25 observations
##   predicted class=B2  expected loss=0.24  P(node) =9.097426e-05
##     class counts:     6    19     0     0     0
##    probabilities: 0.240 0.760 0.000 0.000 0.000 
## 
## Node number 883: 349 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5673352  P(node) =0.001270001
##     class counts:   151   135    46    16     1
##    probabilities: 0.433 0.387 0.132 0.046 0.003 
##   left son=1766 (336 obs) right son=1767 (13 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=2.2574550, (0 missing)
##       age               < 69.5   to the left,  improve=1.5047970, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.0520090, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.8771249, (0 missing)
##       reimbursement2008 < 2535   to the right, improve=0.8193194, (0 missing)
## 
## Node number 886: 1101 observations,    complexity param=7.748763e-05
##   predicted class=B1  expected loss=0.595822  P(node) =0.004006506
##     class counts:   445   444   150    57     5
##    probabilities: 0.404 0.403 0.136 0.052 0.005 
##   left son=1772 (1057 obs) right son=1773 (44 obs)
##   Primary splits:
##       stroke            < 0.5    to the left,  improve=2.0669290, (0 missing)
##       age               < 46.5   to the left,  improve=1.0570040, (0 missing)
##       reimbursement2008 < 2535   to the right, improve=0.9632398, (0 missing)
##       cancer            < 0.5    to the left,  improve=0.9197684, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.7998904, (0 missing)
## 
## Node number 887: 432 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.587963  P(node) =0.001572035
##     class counts:   143   178    73    34     4
##    probabilities: 0.331 0.412 0.169 0.079 0.009 
##   left son=1774 (403 obs) right son=1775 (29 obs)
##   Primary splits:
##       reimbursement2008 < 2215   to the right, improve=1.979831, (0 missing)
##       depression        < 0.5    to the left,  improve=1.399095, (0 missing)
##       age               < 65.5   to the right, improve=1.336452, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.190308, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.062301, (0 missing)
## 
## Node number 900: 117 observations
##   predicted class=B1  expected loss=0.3418803  P(node) =0.0004257595
##     class counts:    77    25     8     7     0
##    probabilities: 0.658 0.214 0.068 0.060 0.000 
## 
## Node number 901: 693 observations,    complexity param=5.258089e-05
##   predicted class=B1  expected loss=0.5165945  P(node) =0.002521807
##     class counts:   335   232   100    26     0
##    probabilities: 0.483 0.335 0.144 0.038 0.000 
##   left son=1802 (684 obs) right son=1803 (9 obs)
##   Primary splits:
##       reimbursement2008 < 11105  to the left,  improve=2.2165640, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.2536740, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.8354877, (0 missing)
##       age               < 34     to the left,  improve=0.8168384, (0 missing)
##       copd              < 0.5    to the left,  improve=0.3325841, (0 missing)
## 
## Node number 904: 1626 observations
##   predicted class=B1  expected loss=0.4391144  P(node) =0.005916966
##     class counts:   912   414   190    99    11
##    probabilities: 0.561 0.255 0.117 0.061 0.007 
## 
## Node number 905: 1678 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.511323  P(node) =0.006106192
##     class counts:   820   565   199    84    10
##    probabilities: 0.489 0.337 0.119 0.050 0.006 
##   left son=1810 (1608 obs) right son=1811 (70 obs)
##   Primary splits:
##       reimbursement2008 < 5695   to the left,  improve=3.4228850, (0 missing)
##       age               < 54.5   to the left,  improve=3.4011680, (0 missing)
##       kidney            < 0.5    to the left,  improve=2.7930360, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.6256038, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.4872841, (0 missing)
## 
## Node number 906: 857 observations,    complexity param=8.855729e-05
##   predicted class=B1  expected loss=0.5425904  P(node) =0.003118598
##     class counts:   392   313   100    48     4
##    probabilities: 0.457 0.365 0.117 0.056 0.005 
##   left son=1812 (405 obs) right son=1813 (452 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the left,  improve=1.8446560, (0 missing)
##       reimbursement2008 < 8165   to the right, improve=1.5511950, (0 missing)
##       age               < 58.5   to the right, improve=1.3750490, (0 missing)
##       bucket2008        < 2.5    to the right, improve=1.0178390, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.9078092, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 7010   to the left,  agree=0.629, adj=0.215, (0 split)
##       bucket2008        < 2.5    to the left,  agree=0.611, adj=0.178, (0 split)
##       kidney            < 0.5    to the left,  agree=0.585, adj=0.121, (0 split)
##       copd              < 0.5    to the left,  agree=0.583, adj=0.119, (0 split)
##       age               < 77.5   to the left,  agree=0.555, adj=0.059, (0 split)
## 
## Node number 907: 105 observations
##   predicted class=B2  expected loss=0.5142857  P(node) =0.0003820919
##     class counts:    32    51    14     7     1
##    probabilities: 0.305 0.486 0.133 0.067 0.010 
## 
## Node number 910: 696 observations,    complexity param=0.0001162314
##   predicted class=B2  expected loss=0.6192529  P(node) =0.002532723
##     class counts:   232   265   112    75    12
##    probabilities: 0.333 0.381 0.161 0.108 0.017 
##   left son=1820 (177 obs) right son=1821 (519 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the left,  improve=3.566495, (0 missing)
##       copd              < 0.5    to the left,  improve=2.688595, (0 missing)
##       age               < 70.5   to the right, improve=1.873250, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.737201, (0 missing)
##       reimbursement2008 < 7405   to the left,  improve=1.699902, (0 missing)
## 
## Node number 911: 10 observations
##   predicted class=B3  expected loss=0.4  P(node) =3.63897e-05
##     class counts:     1     1     6     2     0
##    probabilities: 0.100 0.100 0.600 0.200 0.000 
## 
## Node number 912: 58 observations
##   predicted class=B2  expected loss=0.3793103  P(node) =0.0002110603
##     class counts:    20    36     0     1     1
##    probabilities: 0.345 0.621 0.000 0.017 0.017 
## 
## Node number 913: 382 observations,    complexity param=5.313437e-05
##   predicted class=B1  expected loss=0.5890052  P(node) =0.001390087
##     class counts:   157   156    49    19     1
##    probabilities: 0.411 0.408 0.128 0.050 0.003 
##   left son=1826 (25 obs) right son=1827 (357 obs)
##   Primary splits:
##       reimbursement2008 < 3245   to the left,  improve=2.6514540, (0 missing)
##       age               < 80.5   to the right, improve=2.1655330, (0 missing)
##       depression        < 0.5    to the left,  improve=1.2095670, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.1024610, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.7876318, (0 missing)
## 
## Node number 922: 9 observations
##   predicted class=B1  expected loss=0.3333333  P(node) =3.275073e-05
##     class counts:     6     0     1     1     1
##    probabilities: 0.667 0.000 0.111 0.111 0.111 
## 
## Node number 923: 293 observations,    complexity param=7.748763e-05
##   predicted class=B2  expected loss=0.5938567  P(node) =0.001066218
##     class counts:    96   119    48    30     0
##    probabilities: 0.328 0.406 0.164 0.102 0.000 
##   left son=1846 (39 obs) right son=1847 (254 obs)
##   Primary splits:
##       stroke            < 0.5    to the right, improve=2.1766940, (0 missing)
##       age               < 65.5   to the right, improve=1.5636490, (0 missing)
##       reimbursement2008 < 11660  to the right, improve=1.0372370, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.9901623, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.7188410, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 79760  to the right, agree=0.87, adj=0.026, (0 split)
## 
## Node number 924: 220 observations,    complexity param=6.088314e-05
##   predicted class=B1  expected loss=0.65  P(node) =0.0008005735
##     class counts:    77    66    57    16     4
##    probabilities: 0.350 0.300 0.259 0.073 0.018 
##   left son=1848 (132 obs) right son=1849 (88 obs)
##   Primary splits:
##       reimbursement2008 < 12810  to the right, improve=2.5787880, (0 missing)
##       age               < 76.5   to the left,  improve=2.1718510, (0 missing)
##       bucket2008        < 3.5    to the right, improve=1.0384950, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.8392769, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.7969697, (0 missing)
##   Surrogate splits:
##       bucket2008 < 3.5    to the right, agree=0.636, adj=0.091, (0 split)
## 
## Node number 925: 62 observations
##   predicted class=B2  expected loss=0.5322581  P(node) =0.0002256162
##     class counts:    11    29    14     7     1
##    probabilities: 0.177 0.468 0.226 0.113 0.016 
## 
## Node number 944: 76 observations
##   predicted class=B1  expected loss=0.4736842  P(node) =0.0002765618
##     class counts:    40    18    12     5     1
##    probabilities: 0.526 0.237 0.158 0.066 0.013 
## 
## Node number 945: 83 observations
##   predicted class=B2  expected loss=0.6024096  P(node) =0.0003020345
##     class counts:    25    33    21     3     1
##    probabilities: 0.301 0.398 0.253 0.036 0.012 
## 
## Node number 948: 126 observations
##   predicted class=B2  expected loss=0.5079365  P(node) =0.0004585103
##     class counts:    22    62    33     9     0
##    probabilities: 0.175 0.492 0.262 0.071 0.000 
## 
## Node number 949: 111 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.6486486  P(node) =0.0004039257
##     class counts:    13    39    39    17     3
##    probabilities: 0.117 0.351 0.351 0.153 0.027 
##   left son=1898 (54 obs) right son=1899 (57 obs)
##   Primary splits:
##       age               < 75.5   to the left,  improve=1.9702330, (0 missing)
##       reimbursement2008 < 23940  to the left,  improve=1.5183950, (0 missing)
##       stroke            < 0.5    to the right, improve=1.4096100, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=1.1218360, (0 missing)
##       kidney            < 0.5    to the right, improve=0.9749865, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 36125  to the right, agree=0.586, adj=0.148, (0 split)
##       depression        < 0.5    to the right, agree=0.568, adj=0.111, (0 split)
##       alzheimers        < 0.5    to the right, agree=0.550, adj=0.074, (0 split)
##       bucket2008        < 4.5    to the right, agree=0.532, adj=0.037, (0 split)
## 
## Node number 962: 1636 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5177262  P(node) =0.005953356
##     class counts:   789   562   198    80     7
##    probabilities: 0.482 0.344 0.121 0.049 0.004 
##   left son=1924 (1127 obs) right son=1925 (509 obs)
##   Primary splits:
##       alzheimers        < 0.5    to the left,  improve=2.275005, (0 missing)
##       depression        < 0.5    to the left,  improve=2.200834, (0 missing)
##       age               < 56.5   to the right, improve=2.161392, (0 missing)
##       reimbursement2008 < 3635   to the left,  improve=1.571205, (0 missing)
##       copd              < 0.5    to the left,  improve=1.483908, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 9210   to the left,  agree=0.69, adj=0.004, (0 split)
## 
## Node number 963: 578 observations,    complexity param=0.0001439056
##   predicted class=B2  expected loss=0.5916955  P(node) =0.002103325
##     class counts:   220   236    92    27     3
##    probabilities: 0.381 0.408 0.159 0.047 0.005 
##   left son=1926 (339 obs) right son=1927 (239 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=4.643194, (0 missing)
##       copd              < 0.5    to the left,  improve=3.299852, (0 missing)
##       reimbursement2008 < 4535   to the left,  improve=1.771265, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.392171, (0 missing)
##       age               < 39.5   to the right, improve=1.271983, (0 missing)
##   Surrogate splits:
##       age < 43.5   to the right, agree=0.602, adj=0.038, (0 split)
## 
## Node number 964: 1363 observations,    complexity param=6.088314e-05
##   predicted class=B1  expected loss=0.5561262  P(node) =0.004959917
##     class counts:   605   435   219    92    12
##    probabilities: 0.444 0.319 0.161 0.067 0.009 
##   left son=1928 (798 obs) right son=1929 (565 obs)
##   Primary splits:
##       copd              < 0.5    to the left,  improve=7.963265, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=4.055475, (0 missing)
##       reimbursement2008 < 29005  to the left,  improve=3.506334, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.818093, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.815648, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 55265  to the left,  agree=0.589, adj=0.009, (0 split)
##       bucket2008        < 4.5    to the left,  agree=0.589, adj=0.009, (0 split)
##       age               < 27.5   to the right, agree=0.588, adj=0.005, (0 split)
## 
## Node number 965: 4200 observations,    complexity param=0.0004649258
##   predicted class=B2  expected loss=0.597619  P(node) =0.01528368
##     class counts:  1619  1690   634   236    21
##    probabilities: 0.385 0.402 0.151 0.056 0.005 
##   left son=1930 (1953 obs) right son=1931 (2247 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the left,  improve=7.153470, (0 missing)
##       copd              < 0.5    to the left,  improve=3.796544, (0 missing)
##       age               < 49.5   to the left,  improve=2.763159, (0 missing)
##       reimbursement2008 < 3415   to the left,  improve=2.562311, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.867356, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 4495   to the left,  agree=0.563, adj=0.060, (0 split)
##       copd              < 0.5    to the left,  agree=0.551, adj=0.035, (0 split)
##       alzheimers        < 0.5    to the left,  agree=0.540, adj=0.010, (0 split)
##       age               < 45.5   to the left,  agree=0.536, adj=0.002, (0 split)
## 
## Node number 966: 2928 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5529372  P(node) =0.01065491
##     class counts:   904  1309   506   190    19
##    probabilities: 0.309 0.447 0.173 0.065 0.006 
##   left son=1932 (1987 obs) right son=1933 (941 obs)
##   Primary splits:
##       copd              < 0.5    to the left,  improve=5.088653, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=4.205973, (0 missing)
##       reimbursement2008 < 8045   to the left,  improve=3.909055, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.356901, (0 missing)
##       stroke            < 0.5    to the left,  improve=3.071040, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 8235   to the left,  agree=0.68, adj=0.005, (0 split)
## 
## Node number 967: 1464 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.6277322  P(node) =0.005327453
##     class counts:   475   545   290   146     8
##    probabilities: 0.324 0.372 0.198 0.100 0.005 
##   left son=1934 (36 obs) right son=1935 (1428 obs)
##   Primary splits:
##       reimbursement2008 < 8485   to the left,  improve=3.191750, (0 missing)
##       age               < 78.5   to the left,  improve=2.281932, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=2.180745, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.944689, (0 missing)
##       copd              < 0.5    to the left,  improve=1.512341, (0 missing)
## 
## Node number 976: 102 observations
##   predicted class=B1  expected loss=0.4803922  P(node) =0.000371175
##     class counts:    53    28    13     7     1
##    probabilities: 0.520 0.275 0.127 0.069 0.010 
## 
## Node number 977: 961 observations
##   predicted class=B2  expected loss=0.5848075  P(node) =0.003497051
##     class counts:   283   399   179    88    12
##    probabilities: 0.294 0.415 0.186 0.092 0.012 
## 
## Node number 992: 572 observations,    complexity param=0.0001411382
##   predicted class=B1  expected loss=0.5909091  P(node) =0.002081491
##     class counts:   234   183    92    57     6
##    probabilities: 0.409 0.320 0.161 0.100 0.010 
##   left son=1984 (101 obs) right son=1985 (471 obs)
##   Primary splits:
##       reimbursement2008 < 3545   to the left,  improve=4.251847, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=2.618377, (0 missing)
##       age               < 69.5   to the right, improve=2.566628, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.664728, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.532473, (0 missing)
## 
## Node number 993: 392 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.5790816  P(node) =0.001426476
##     class counts:   122   165    75    25     5
##    probabilities: 0.311 0.421 0.191 0.064 0.013 
##   left son=1986 (9 obs) right son=1987 (383 obs)
##   Primary splits:
##       reimbursement2008 < 14460  to the right, improve=2.744913, (0 missing)
##       age               < 48.5   to the left,  improve=1.556717, (0 missing)
##       copd              < 0.5    to the left,  improve=1.522824, (0 missing)
##       bucket2008        < 2.5    to the right, improve=1.319075, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.292043, (0 missing)
## 
## Node number 994: 3172 observations
##   predicted class=B2  expected loss=0.5630517  P(node) =0.01154281
##     class counts:   709  1386   688   337    52
##    probabilities: 0.224 0.437 0.217 0.106 0.016 
## 
## Node number 995: 3650 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.6326027  P(node) =0.01328224
##     class counts:   917  1341   812   510    70
##    probabilities: 0.251 0.367 0.222 0.140 0.019 
##   left son=1990 (2424 obs) right son=1991 (1226 obs)
##   Primary splits:
##       osteoporosis  < 0.5    to the left,  improve=8.187206, (0 missing)
##       depression    < 0.5    to the left,  improve=5.387571, (0 missing)
##       copd          < 0.5    to the left,  improve=5.189054, (0 missing)
##       alzheimers    < 0.5    to the left,  improve=3.710849, (0 missing)
##       heart.failure < 0.5    to the left,  improve=2.971629, (0 missing)
## 
## Node number 1008: 973 observations
##   predicted class=B2  expected loss=0.5611511  P(node) =0.003540718
##     class counts:   160   427   184   174    28
##    probabilities: 0.164 0.439 0.189 0.179 0.029 
## 
## Node number 1009: 318 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5283019  P(node) =0.001157193
##     class counts:    23   150    96    45     4
##    probabilities: 0.072 0.472 0.302 0.142 0.013 
##   left son=2018 (293 obs) right son=2019 (25 obs)
##   Primary splits:
##       reimbursement2008 < 16525  to the right, improve=7.797134, (0 missing)
##       ihd               < 0.5    to the left,  improve=3.194748, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.170376, (0 missing)
##       bucket2008        < 3.5    to the right, improve=1.159119, (0 missing)
##       age               < 55     to the right, improve=1.113448, (0 missing)
## 
## Node number 1014: 1286 observations
##   predicted class=B2  expected loss=0.6251944  P(node) =0.004679716
##     class counts:    74   482   303   348    79
##    probabilities: 0.058 0.375 0.236 0.271 0.061 
## 
## Node number 1015: 251 observations,    complexity param=6.088314e-05
##   predicted class=B4  expected loss=0.6095618  P(node) =0.0009133816
##     class counts:    13    77    50    98    13
##    probabilities: 0.052 0.307 0.199 0.390 0.052 
##   left son=2030 (237 obs) right son=2031 (14 obs)
##   Primary splits:
##       reimbursement2008 < 101585 to the left,  improve=3.425401, (0 missing)
##       age               < 61.5   to the left,  improve=2.440583, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=2.158559, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=2.020557, (0 missing)
##       cancer            < 0.5    to the right, improve=1.778561, (0 missing)
## 
## Node number 1016: 1317 observations,    complexity param=0.0001439056
##   predicted class=B2  expected loss=0.6757783  P(node) =0.004792524
##     class counts:   269   427   234   313    74
##    probabilities: 0.204 0.324 0.178 0.238 0.056 
##   left son=2032 (72 obs) right son=2033 (1245 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=5.587185, (0 missing)
##       reimbursement2008 < 22435  to the left,  improve=5.039175, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=3.150989, (0 missing)
##       age               < 50.5   to the right, improve=2.709964, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=2.161876, (0 missing)
## 
## Node number 1017: 1172 observations,    complexity param=0.0002036818
##   predicted class=B4  expected loss=0.6953925  P(node) =0.004264873
##     class counts:   272   265   202   357    76
##    probabilities: 0.232 0.226 0.172 0.305 0.065 
##   left son=2034 (191 obs) right son=2035 (981 obs)
##   Primary splits:
##       reimbursement2008 < 43640  to the right, improve=6.105110, (0 missing)
##       bucket2008        < 4.5    to the right, improve=4.295055, (0 missing)
##       ihd               < 0.5    to the left,  improve=2.740224, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=2.395917, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.864237, (0 missing)
##   Surrogate splits:
##       bucket2008 < 4.5    to the right, agree=0.925, adj=0.539, (0 split)
## 
## Node number 1652: 555 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5441441  P(node) =0.002019629
##     class counts:   253   193    76    29     4
##    probabilities: 0.456 0.348 0.137 0.052 0.007 
##   left son=3304 (265 obs) right son=3305 (290 obs)
##   Primary splits:
##       reimbursement2008 < 1955   to the left,  improve=2.2492970, (0 missing)
##       age               < 44.5   to the right, improve=2.2010530, (0 missing)
##       cancer            < 0.5    to the left,  improve=2.0271900, (0 missing)
##       ihd               < 0.5    to the left,  improve=2.0227740, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.6965966, (0 missing)
##   Surrogate splits:
##       ihd    < 0.5    to the left,  agree=0.539, adj=0.034, (0 split)
##       age    < 90.5   to the right, agree=0.532, adj=0.019, (0 split)
##       cancer < 0.5    to the right, agree=0.528, adj=0.011, (0 split)
## 
## Node number 1653: 41 observations
##   predicted class=B2  expected loss=0.4878049  P(node) =0.0001491978
##     class counts:    13    21     1     5     1
##    probabilities: 0.317 0.512 0.024 0.122 0.024 
## 
## Node number 1696: 263 observations
##   predicted class=B1  expected loss=0.4980989  P(node) =0.0009570492
##     class counts:   132    94    26    11     0
##    probabilities: 0.502 0.357 0.099 0.042 0.000 
## 
## Node number 1697: 122 observations
##   predicted class=B2  expected loss=0.5737705  P(node) =0.0004439544
##     class counts:    43    52    13    12     2
##    probabilities: 0.352 0.426 0.107 0.098 0.016 
## 
## Node number 1734: 171 observations
##   predicted class=B1  expected loss=0.4736842  P(node) =0.0006222639
##     class counts:    90    46    24    10     1
##    probabilities: 0.526 0.269 0.140 0.058 0.006 
## 
## Node number 1735: 1057 observations,    complexity param=6.918538e-05
##   predicted class=B1  expected loss=0.5666982  P(node) =0.003846392
##     class counts:   458   410   137    47     5
##    probabilities: 0.433 0.388 0.130 0.044 0.005 
##   left son=3470 (840 obs) right son=3471 (217 obs)
##   Primary splits:
##       age               < 83.5   to the left,  improve=3.809819, (0 missing)
##       kidney            < 0.5    to the left,  improve=2.564065, (0 missing)
##       depression        < 0.5    to the left,  improve=1.351420, (0 missing)
##       copd              < 0.5    to the left,  improve=1.145117, (0 missing)
##       reimbursement2008 < 2975   to the left,  improve=1.026292, (0 missing)
## 
## Node number 1750: 1752 observations,    complexity param=0.0001129105
##   predicted class=B1  expected loss=0.6084475  P(node) =0.006375476
##     class counts:   686   629   294   131    12
##    probabilities: 0.392 0.359 0.168 0.075 0.007 
##   left son=3500 (1099 obs) right son=3501 (653 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=3.008256, (0 missing)
##       age               < 97.5   to the left,  improve=2.167182, (0 missing)
##       reimbursement2008 < 3055   to the left,  improve=1.739250, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.536121, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.306511, (0 missing)
##   Surrogate splits:
##       age < 41.5   to the right, agree=0.628, adj=0.002, (0 split)
## 
## Node number 1751: 39 observations
##   predicted class=B2  expected loss=0.4615385  P(node) =0.0001419198
##     class counts:     6    21     6     6     0
##    probabilities: 0.154 0.538 0.154 0.154 0.000 
## 
## Node number 1752: 112 observations
##   predicted class=B1  expected loss=0.5  P(node) =0.0004075647
##     class counts:    56    33    14     9     0
##    probabilities: 0.500 0.295 0.125 0.080 0.000 
## 
## Node number 1753: 68 observations
##   predicted class=B2  expected loss=0.4852941  P(node) =0.00024745
##     class counts:    22    35     9     2     0
##    probabilities: 0.324 0.515 0.132 0.029 0.000 
## 
## Node number 1754: 403 observations,    complexity param=6.272808e-05
##   predicted class=B1  expected loss=0.6253102  P(node) =0.001466505
##     class counts:   151   147    79    20     6
##    probabilities: 0.375 0.365 0.196 0.050 0.015 
##   left son=3508 (382 obs) right son=3509 (21 obs)
##   Primary splits:
##       reimbursement2008 < 2585   to the left,  improve=1.3835870, (0 missing)
##       age               < 45.5   to the left,  improve=1.1912610, (0 missing)
##       depression        < 0.5    to the left,  improve=0.8974996, (0 missing)
##       cancer            < 0.5    to the right, improve=0.7248908, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.2961750, (0 missing)
## 
## Node number 1755: 30 observations
##   predicted class=B2  expected loss=0.6333333  P(node) =0.0001091691
##     class counts:     5    11     9     5     0
##    probabilities: 0.167 0.367 0.300 0.167 0.000 
## 
## Node number 1766: 336 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5535714  P(node) =0.001222694
##     class counts:   150   128    44    14     0
##    probabilities: 0.446 0.381 0.131 0.042 0.000 
##   left son=3532 (322 obs) right son=3533 (14 obs)
##   Primary splits:
##       age               < 90.5   to the left,  improve=1.4565220, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.1063780, (0 missing)
##       reimbursement2008 < 2325   to the right, improve=0.8683190, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.8476676, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.4098214, (0 missing)
## 
## Node number 1767: 13 observations
##   predicted class=B2  expected loss=0.4615385  P(node) =4.730662e-05
##     class counts:     1     7     2     2     1
##    probabilities: 0.077 0.538 0.154 0.154 0.077 
## 
## Node number 1772: 1057 observations,    complexity param=7.748763e-05
##   predicted class=B1  expected loss=0.589404  P(node) =0.003846392
##     class counts:   434   420   145    54     4
##    probabilities: 0.411 0.397 0.137 0.051 0.004 
##   left son=3544 (1008 obs) right son=3545 (49 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=1.0144070, (0 missing)
##       age               < 46.5   to the left,  improve=1.0088960, (0 missing)
##       reimbursement2008 < 2535   to the right, improve=0.9481247, (0 missing)
##       copd              < 0.5    to the left,  improve=0.6576908, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.5672579, (0 missing)
## 
## Node number 1773: 44 observations
##   predicted class=B2  expected loss=0.4545455  P(node) =0.0001601147
##     class counts:    11    24     5     3     1
##    probabilities: 0.250 0.545 0.114 0.068 0.023 
## 
## Node number 1774: 403 observations
##   predicted class=B2  expected loss=0.5756824  P(node) =0.001466505
##     class counts:   130   171    70    28     4
##    probabilities: 0.323 0.424 0.174 0.069 0.010 
## 
## Node number 1775: 29 observations
##   predicted class=B1  expected loss=0.5517241  P(node) =0.0001055301
##     class counts:    13     7     3     6     0
##    probabilities: 0.448 0.241 0.103 0.207 0.000 
## 
## Node number 1802: 684 observations,    complexity param=5.258089e-05
##   predicted class=B1  expected loss=0.5146199  P(node) =0.002489056
##     class counts:   332   231    95    26     0
##    probabilities: 0.485 0.338 0.139 0.038 0.000 
##   left son=3604 (286 obs) right son=3605 (398 obs)
##   Primary splits:
##       reimbursement2008 < 4365   to the left,  improve=1.4254390, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.1902870, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.8341619, (0 missing)
##       age               < 34     to the left,  improve=0.8175360, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.3590913, (0 missing)
## 
## Node number 1803: 9 observations
##   predicted class=B3  expected loss=0.4444444  P(node) =3.275073e-05
##     class counts:     3     1     5     0     0
##    probabilities: 0.333 0.111 0.556 0.000 0.000 
## 
## Node number 1810: 1608 observations
##   predicted class=B1  expected loss=0.5062189  P(node) =0.005851465
##     class counts:   794   529   193    82    10
##    probabilities: 0.494 0.329 0.120 0.051 0.006 
## 
## Node number 1811: 70 observations
##   predicted class=B2  expected loss=0.4857143  P(node) =0.0002547279
##     class counts:    26    36     6     2     0
##    probabilities: 0.371 0.514 0.086 0.029 0.000 
## 
## Node number 1812: 405 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5012346  P(node) =0.001473783
##     class counts:   202   140    42    18     3
##    probabilities: 0.499 0.346 0.104 0.044 0.007 
##   left son=3624 (329 obs) right son=3625 (76 obs)
##   Primary splits:
##       age               < 83.5   to the left,  improve=1.8474760, (0 missing)
##       reimbursement2008 < 14045  to the left,  improve=1.4437850, (0 missing)
##       kidney            < 0.5    to the right, improve=1.0197570, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.4573240, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.4260458, (0 missing)
## 
## Node number 1813: 452 observations,    complexity param=8.855729e-05
##   predicted class=B1  expected loss=0.579646  P(node) =0.001644815
##     class counts:   190   173    58    30     1
##    probabilities: 0.420 0.383 0.128 0.066 0.002 
##   left son=3626 (362 obs) right son=3627 (90 obs)
##   Primary splits:
##       reimbursement2008 < 3875   to the right, improve=2.645100, (0 missing)
##       age               < 84.5   to the left,  improve=2.429876, (0 missing)
##       bucket2008        < 2.5    to the right, improve=1.612268, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.063100, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.663279, (0 missing)
##   Surrogate splits:
##       age < 32     to the right, agree=0.803, adj=0.011, (0 split)
## 
## Node number 1820: 177 observations
##   predicted class=B1  expected loss=0.559322  P(node) =0.0006440978
##     class counts:    78    62    26    11     0
##    probabilities: 0.441 0.350 0.147 0.062 0.000 
## 
## Node number 1821: 519 observations
##   predicted class=B2  expected loss=0.6088632  P(node) =0.001888626
##     class counts:   154   203    86    64    12
##    probabilities: 0.297 0.391 0.166 0.123 0.023 
## 
## Node number 1826: 25 observations
##   predicted class=B1  expected loss=0.32  P(node) =9.097426e-05
##     class counts:    17     7     1     0     0
##    probabilities: 0.680 0.280 0.040 0.000 0.000 
## 
## Node number 1827: 357 observations,    complexity param=5.313437e-05
##   predicted class=B2  expected loss=0.5826331  P(node) =0.001299112
##     class counts:   140   149    48    19     1
##    probabilities: 0.392 0.417 0.134 0.053 0.003 
##   left son=3654 (91 obs) right son=3655 (266 obs)
##   Primary splits:
##       age               < 80.5   to the right, improve=2.1730570, (0 missing)
##       reimbursement2008 < 4405   to the right, improve=2.0106540, (0 missing)
##       depression        < 0.5    to the left,  improve=0.7793758, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.7738464, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.6298514, (0 missing)
## 
## Node number 1846: 39 observations
##   predicted class=B1  expected loss=0.4871795  P(node) =0.0001419198
##     class counts:    20    12     4     3     0
##    probabilities: 0.513 0.308 0.103 0.077 0.000 
## 
## Node number 1847: 254 observations
##   predicted class=B2  expected loss=0.5787402  P(node) =0.0009242985
##     class counts:    76   107    44    27     0
##    probabilities: 0.299 0.421 0.173 0.106 0.000 
## 
## Node number 1848: 132 observations,    complexity param=6.088314e-05
##   predicted class=B1  expected loss=0.5909091  P(node) =0.0004803441
##     class counts:    54    42    26     9     1
##    probabilities: 0.409 0.318 0.197 0.068 0.008 
##   left son=3696 (105 obs) right son=3697 (27 obs)
##   Primary splits:
##       age               < 84.5   to the left,  improve=4.2569990, (0 missing)
##       reimbursement2008 < 13440  to the left,  improve=1.5425260, (0 missing)
##       ihd               < 0.5    to the right, improve=1.3693110, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.4363743, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.4353832, (0 missing)
## 
## Node number 1849: 88 observations
##   predicted class=B3  expected loss=0.6477273  P(node) =0.0003202294
##     class counts:    23    24    31     7     3
##    probabilities: 0.261 0.273 0.352 0.080 0.034 
## 
## Node number 1898: 54 observations
##   predicted class=B3  expected loss=0.5555556  P(node) =0.0001965044
##     class counts:     8    14    24     7     1
##    probabilities: 0.148 0.259 0.444 0.130 0.019 
## 
## Node number 1899: 57 observations
##   predicted class=B2  expected loss=0.5614035  P(node) =0.0002074213
##     class counts:     5    25    15    10     2
##    probabilities: 0.088 0.439 0.263 0.175 0.035 
## 
## Node number 1924: 1127 observations
##   predicted class=B1  expected loss=0.4960071  P(node) =0.00410112
##     class counts:   568   377   131    47     4
##    probabilities: 0.504 0.335 0.116 0.042 0.004 
## 
## Node number 1925: 509 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5658153  P(node) =0.001852236
##     class counts:   221   185    67    33     3
##    probabilities: 0.434 0.363 0.132 0.065 0.006 
##   left son=3850 (137 obs) right son=3851 (372 obs)
##   Primary splits:
##       reimbursement2008 < 3775   to the left,  improve=1.6880360, (0 missing)
##       depression        < 0.5    to the left,  improve=1.6361880, (0 missing)
##       age               < 96.5   to the left,  improve=1.5026800, (0 missing)
##       copd              < 0.5    to the left,  improve=1.2566690, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.7418596, (0 missing)
## 
## Node number 1926: 339 observations,    complexity param=9.409212e-05
##   predicted class=B1  expected loss=0.5575221  P(node) =0.001233611
##     class counts:   150   127    45    16     1
##    probabilities: 0.442 0.375 0.133 0.047 0.003 
##   left son=3852 (211 obs) right son=3853 (128 obs)
##   Primary splits:
##       reimbursement2008 < 4905   to the left,  improve=1.6963240, (0 missing)
##       age               < 45     to the right, improve=1.4829560, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.2573130, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.6055009, (0 missing)
##       copd              < 0.5    to the left,  improve=0.2708942, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.687, adj=0.172, (0 split)
##       copd       < 0.5    to the left,  agree=0.664, adj=0.109, (0 split)
##       stroke     < 0.5    to the left,  agree=0.652, adj=0.078, (0 split)
## 
## Node number 1927: 239 observations,    complexity param=8.855729e-05
##   predicted class=B2  expected loss=0.5439331  P(node) =0.0008697139
##     class counts:    70   109    47    11     2
##    probabilities: 0.293 0.456 0.197 0.046 0.008 
##   left son=3854 (181 obs) right son=3855 (58 obs)
##   Primary splits:
##       copd              < 0.5    to the left,  improve=6.3468870, (0 missing)
##       reimbursement2008 < 5790   to the right, improve=1.7891020, (0 missing)
##       age               < 60.5   to the left,  improve=1.2691270, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.8740764, (0 missing)
##       stroke            < 0.5    to the right, improve=0.6684821, (0 missing)
##   Surrogate splits:
##       age < 35     to the right, agree=0.762, adj=0.017, (0 split)
## 
## Node number 1928: 798 observations
##   predicted class=B1  expected loss=0.5075188  P(node) =0.002903898
##     class counts:   393   256    95    51     3
##    probabilities: 0.492 0.321 0.119 0.064 0.004 
## 
## Node number 1929: 565 observations,    complexity param=6.088314e-05
##   predicted class=B1  expected loss=0.6247788  P(node) =0.002056018
##     class counts:   212   179   124    41     9
##    probabilities: 0.375 0.317 0.219 0.073 0.016 
##   left son=3858 (116 obs) right son=3859 (449 obs)
##   Primary splits:
##       stroke            < 0.5    to the right, improve=4.0381830, (0 missing)
##       reimbursement2008 < 31655  to the left,  improve=2.7523450, (0 missing)
##       age               < 42.5   to the right, improve=1.7655450, (0 missing)
##       bucket2008        < 4.5    to the left,  improve=1.4692280, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.5615109, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 61780  to the right, agree=0.8, adj=0.026, (0 split)
## 
## Node number 1930: 1953 observations,    complexity param=9.962695e-05
##   predicted class=B1  expected loss=0.578085  P(node) =0.007106909
##     class counts:   824   782   251    88     8
##    probabilities: 0.422 0.400 0.129 0.045 0.004 
##   left son=3860 (343 obs) right son=3861 (1610 obs)
##   Primary splits:
##       reimbursement2008 < 3415   to the left,  improve=3.4037160, (0 missing)
##       age               < 42.5   to the left,  improve=3.2783080, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.6509623, (0 missing)
##       copd              < 0.5    to the left,  improve=0.5598170, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.2946889, (0 missing)
## 
## Node number 1931: 2247 observations,    complexity param=0.0001605101
##   predicted class=B2  expected loss=0.5959057  P(node) =0.008176767
##     class counts:   795   908   383   148    13
##    probabilities: 0.354 0.404 0.170 0.066 0.006 
##   left son=3862 (866 obs) right son=3863 (1381 obs)
##   Primary splits:
##       reimbursement2008 < 5335   to the right, improve=3.344298, (0 missing)
##       copd              < 0.5    to the left,  improve=2.798571, (0 missing)
##       age               < 68.5   to the left,  improve=2.255236, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.653597, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.448247, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.678, adj=0.165, (0 split)
##       age        < 34.5   to the left,  agree=0.616, adj=0.005, (0 split)
## 
## Node number 1932: 1987 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5546049  P(node) =0.007230634
##     class counts:   662   885   320   111     9
##    probabilities: 0.333 0.445 0.161 0.056 0.005 
##   left son=3864 (1964 obs) right son=3865 (23 obs)
##   Primary splits:
##       age               < 98.5   to the left,  improve=3.328502, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.141909, (0 missing)
##       reimbursement2008 < 3085   to the left,  improve=3.126917, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=2.906536, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.332632, (0 missing)
## 
## Node number 1933: 941 observations
##   predicted class=B2  expected loss=0.5494155  P(node) =0.003424271
##     class counts:   242   424   186    79    10
##    probabilities: 0.257 0.451 0.198 0.084 0.011 
## 
## Node number 1934: 36 observations
##   predicted class=B1  expected loss=0.4444444  P(node) =0.0001310029
##     class counts:    20     8     8     0     0
##    probabilities: 0.556 0.222 0.222 0.000 0.000 
## 
## Node number 1935: 1428 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.6239496  P(node) =0.00519645
##     class counts:   455   537   282   146     8
##    probabilities: 0.319 0.376 0.197 0.102 0.006 
##   left son=3870 (837 obs) right son=3871 (591 obs)
##   Primary splits:
##       age               < 78.5   to the left,  improve=2.474561, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=2.118405, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.930317, (0 missing)
##       copd              < 0.5    to the left,  improve=1.447977, (0 missing)
##       reimbursement2008 < 8670   to the right, improve=1.324274, (0 missing)
## 
## Node number 1984: 101 observations
##   predicted class=B2  expected loss=0.5247525  P(node) =0.000367536
##     class counts:    33    48    15     4     1
##    probabilities: 0.327 0.475 0.149 0.040 0.010 
## 
## Node number 1985: 471 observations,    complexity param=5.811572e-05
##   predicted class=B1  expected loss=0.5732484  P(node) =0.001713955
##     class counts:   201   135    77    53     5
##    probabilities: 0.427 0.287 0.163 0.113 0.011 
##   left son=3970 (346 obs) right son=3971 (125 obs)
##   Primary splits:
##       osteoporosis      < 0.5    to the left,  improve=2.506365, (0 missing)
##       reimbursement2008 < 11515  to the left,  improve=2.004779, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.922393, (0 missing)
##       age               < 72.5   to the right, improve=1.840715, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=1.312872, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 3600   to the right, agree=0.737, adj=0.008, (0 split)
## 
## Node number 1986: 9 observations
##   predicted class=B1  expected loss=0.2222222  P(node) =3.275073e-05
##     class counts:     7     2     0     0     0
##    probabilities: 0.778 0.222 0.000 0.000 0.000 
## 
## Node number 1987: 383 observations
##   predicted class=B2  expected loss=0.5744125  P(node) =0.001393726
##     class counts:   115   163    75    25     5
##    probabilities: 0.300 0.426 0.196 0.065 0.013 
## 
## Node number 1990: 2424 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.6575908  P(node) =0.008820864
##     class counts:   663   830   547   335    49
##    probabilities: 0.274 0.342 0.226 0.138 0.020 
##   left son=3980 (1234 obs) right son=3981 (1190 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=3.612677, (0 missing)
##       age               < 67.5   to the right, improve=3.329297, (0 missing)
##       copd              < 0.5    to the left,  improve=3.109296, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=2.737555, (0 missing)
##       reimbursement2008 < 9205   to the right, improve=2.610291, (0 missing)
##   Surrogate splits:
##       alzheimers        < 0.5    to the left,  agree=0.552, adj=0.087, (0 split)
##       copd              < 0.5    to the left,  agree=0.540, adj=0.064, (0 split)
##       age               < 53.5   to the right, agree=0.526, adj=0.035, (0 split)
##       reimbursement2008 < 12525  to the left,  agree=0.522, adj=0.026, (0 split)
##       heart.failure     < 0.5    to the left,  agree=0.520, adj=0.023, (0 split)
## 
## Node number 1991: 1226 observations
##   predicted class=B2  expected loss=0.5831974  P(node) =0.004461378
##     class counts:   254   511   265   175    21
##    probabilities: 0.207 0.417 0.216 0.143 0.017 
## 
## Node number 2018: 293 observations
##   predicted class=B2  expected loss=0.4914676  P(node) =0.001066218
##     class counts:    20   149    81    39     4
##    probabilities: 0.068 0.509 0.276 0.133 0.014 
## 
## Node number 2019: 25 observations
##   predicted class=B3  expected loss=0.4  P(node) =9.097426e-05
##     class counts:     3     1    15     6     0
##    probabilities: 0.120 0.040 0.600 0.240 0.000 
## 
## Node number 2030: 237 observations,    complexity param=6.088314e-05
##   predicted class=B4  expected loss=0.6329114  P(node) =0.000862436
##     class counts:    13    76    49    87    12
##    probabilities: 0.055 0.321 0.207 0.367 0.051 
##   left son=4060 (62 obs) right son=4061 (175 obs)
##   Primary splits:
##       cancer            < 0.5    to the right, improve=2.618202, (0 missing)
##       reimbursement2008 < 90420  to the right, improve=2.488954, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=2.039633, (0 missing)
##       age               < 61.5   to the left,  improve=1.881916, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.753135, (0 missing)
## 
## Node number 2031: 14 observations
##   predicted class=B4  expected loss=0.2142857  P(node) =5.094559e-05
##     class counts:     0     1     1    11     1
##    probabilities: 0.000 0.071 0.071 0.786 0.071 
## 
## Node number 2032: 72 observations
##   predicted class=B1  expected loss=0.5694444  P(node) =0.0002620059
##     class counts:    31    18    11     8     4
##    probabilities: 0.431 0.250 0.153 0.111 0.056 
## 
## Node number 2033: 1245 observations
##   predicted class=B2  expected loss=0.6714859  P(node) =0.004530518
##     class counts:   238   409   223   305    70
##    probabilities: 0.191 0.329 0.179 0.245 0.056 
## 
## Node number 2034: 191 observations,    complexity param=7.748763e-05
##   predicted class=B2  expected loss=0.6753927  P(node) =0.0006950434
##     class counts:    29    62    44    42    14
##    probabilities: 0.152 0.325 0.230 0.220 0.073 
##   left son=4068 (172 obs) right son=4069 (19 obs)
##   Primary splits:
##       age               < 64.5   to the right, improve=2.5420870, (0 missing)
##       reimbursement2008 < 71460  to the left,  improve=2.3514440, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.2597430, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.9221356, (0 missing)
##       ihd               < 0.5    to the right, improve=0.7384918, (0 missing)
## 
## Node number 2035: 981 observations,    complexity param=9.962695e-05
##   predicted class=B4  expected loss=0.6788991  P(node) =0.00356983
##     class counts:   243   203   158   315    62
##    probabilities: 0.248 0.207 0.161 0.321 0.063 
##   left son=4070 (468 obs) right son=4071 (513 obs)
##   Primary splits:
##       reimbursement2008 < 23175  to the left,  improve=5.196818, (0 missing)
##       alzheimers        < 0.5    to the right, improve=3.174409, (0 missing)
##       ihd               < 0.5    to the left,  improve=2.640760, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.689264, (0 missing)
##       age               < 97.5   to the right, improve=1.688586, (0 missing)
##   Surrogate splits:
##       bucket2008    < 3.5    to the left,  agree=0.777, adj=0.532, (0 split)
##       heart.failure < 0.5    to the left,  agree=0.534, adj=0.024, (0 split)
##       age           < 53.5   to the left,  agree=0.531, adj=0.017, (0 split)
##       stroke        < 0.5    to the right, agree=0.525, adj=0.004, (0 split)
## 
## Node number 3304: 265 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.490566  P(node) =0.0009643272
##     class counts:   135    82    34    13     1
##    probabilities: 0.509 0.309 0.128 0.049 0.004 
##   left son=6608 (251 obs) right son=6609 (14 obs)
##   Primary splits:
##       stroke            < 0.5    to the left,  improve=3.807509, (0 missing)
##       ihd               < 0.5    to the left,  improve=2.996787, (0 missing)
##       cancer            < 0.5    to the left,  improve=2.863288, (0 missing)
##       reimbursement2008 < 1815   to the left,  improve=1.417998, (0 missing)
##       depression        < 0.5    to the left,  improve=1.227469, (0 missing)
## 
## Node number 3305: 290 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5931034  P(node) =0.001055301
##     class counts:   118   111    42    16     3
##    probabilities: 0.407 0.383 0.145 0.055 0.010 
##   left son=6610 (213 obs) right son=6611 (77 obs)
##   Primary splits:
##       age               < 81.5   to the left,  improve=1.9355560, (0 missing)
##       reimbursement2008 < 2015   to the right, improve=1.1719950, (0 missing)
##       ihd               < 0.5    to the right, improve=0.8443893, (0 missing)
##       stroke            < 0.5    to the right, improve=0.5640543, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.4757090, (0 missing)
## 
## Node number 3470: 840 observations,    complexity param=6.088314e-05
##   predicted class=B1  expected loss=0.5452381  P(node) =0.003056735
##     class counts:   382   309   103    41     5
##    probabilities: 0.455 0.368 0.123 0.049 0.006 
##   left son=6940 (71 obs) right son=6941 (769 obs)
##   Primary splits:
##       age               < 54.5   to the left,  improve=2.5793890, (0 missing)
##       kidney            < 0.5    to the left,  improve=2.0319410, (0 missing)
##       depression        < 0.5    to the left,  improve=1.3091120, (0 missing)
##       reimbursement2008 < 2945   to the right, improve=1.1530320, (0 missing)
##       copd              < 0.5    to the left,  improve=0.9762638, (0 missing)
## 
## Node number 3471: 217 observations
##   predicted class=B2  expected loss=0.5345622  P(node) =0.0007896566
##     class counts:    76   101    34     6     0
##    probabilities: 0.350 0.465 0.157 0.028 0.000 
## 
## Node number 3500: 1099 observations,    complexity param=9.962695e-05
##   predicted class=B1  expected loss=0.5814377  P(node) =0.003999229
##     class counts:   460   388   168    76     7
##    probabilities: 0.419 0.353 0.153 0.069 0.006 
##   left son=7000 (1074 obs) right son=7001 (25 obs)
##   Primary splits:
##       age               < 95.5   to the left,  improve=2.515661, (0 missing)
##       copd              < 0.5    to the left,  improve=2.359857, (0 missing)
##       cancer            < 0.5    to the left,  improve=1.641148, (0 missing)
##       reimbursement2008 < 2575   to the right, improve=1.347245, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.145174, (0 missing)
## 
## Node number 3501: 653 observations,    complexity param=0.0001129105
##   predicted class=B2  expected loss=0.6309342  P(node) =0.002376248
##     class counts:   226   241   126    55     5
##    probabilities: 0.346 0.369 0.193 0.084 0.008 
##   left son=7002 (303 obs) right son=7003 (350 obs)
##   Primary splits:
##       reimbursement2008 < 2655   to the left,  improve=2.636734, (0 missing)
##       cancer            < 0.5    to the left,  improve=1.461370, (0 missing)
##       age               < 55.5   to the right, improve=1.350106, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.189997, (0 missing)
##       bucket2008        < 1.5    to the left,  improve=1.091246, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the left,  agree=0.548, adj=0.026, (0 split)
##       copd       < 0.5    to the right, agree=0.542, adj=0.013, (0 split)
##       age        < 47.5   to the left,  agree=0.539, adj=0.007, (0 split)
## 
## Node number 3508: 382 observations,    complexity param=6.272808e-05
##   predicted class=B2  expected loss=0.6230366  P(node) =0.001390087
##     class counts:   142   144    74    18     4
##    probabilities: 0.372 0.377 0.194 0.047 0.010 
##   left son=7016 (229 obs) right son=7017 (153 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=0.9679873, (0 missing)
##       reimbursement2008 < 2275   to the left,  improve=0.8225283, (0 missing)
##       age               < 75.5   to the right, improve=0.7274055, (0 missing)
##       cancer            < 0.5    to the right, improve=0.6895810, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.4333447, (0 missing)
##   Surrogate splits:
##       alzheimers < 0.5    to the left,  agree=0.620, adj=0.052, (0 split)
##       age        < 50.5   to the right, agree=0.613, adj=0.033, (0 split)
## 
## Node number 3509: 21 observations
##   predicted class=B1  expected loss=0.5714286  P(node) =7.641838e-05
##     class counts:     9     3     5     2     2
##    probabilities: 0.429 0.143 0.238 0.095 0.095 
## 
## Node number 3532: 322 observations
##   predicted class=B1  expected loss=0.5465839  P(node) =0.001171748
##     class counts:   146   119    43    14     0
##    probabilities: 0.453 0.370 0.134 0.043 0.000 
## 
## Node number 3533: 14 observations
##   predicted class=B2  expected loss=0.3571429  P(node) =5.094559e-05
##     class counts:     4     9     1     0     0
##    probabilities: 0.286 0.643 0.071 0.000 0.000 
## 
## Node number 3544: 1008 observations,    complexity param=7.748763e-05
##   predicted class=B1  expected loss=0.5853175  P(node) =0.003668082
##     class counts:   418   400   133    53     4
##    probabilities: 0.415 0.397 0.132 0.053 0.004 
##   left son=7088 (275 obs) right son=7089 (733 obs)
##   Primary splits:
##       reimbursement2008 < 2535   to the right, improve=0.9732083, (0 missing)
##       age               < 39     to the left,  improve=0.9699606, (0 missing)
##       copd              < 0.5    to the left,  improve=0.8468269, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.4615681, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.4416739, (0 missing)
##   Surrogate splits:
##       age < 36.5   to the left,  agree=0.728, adj=0.004, (0 split)
## 
## Node number 3545: 49 observations
##   predicted class=B2  expected loss=0.5918367  P(node) =0.0001783096
##     class counts:    16    20    12     1     0
##    probabilities: 0.327 0.408 0.245 0.020 0.000 
## 
## Node number 3604: 286 observations
##   predicted class=B1  expected loss=0.4685315  P(node) =0.001040746
##     class counts:   152    94    35     5     0
##    probabilities: 0.531 0.329 0.122 0.017 0.000 
## 
## Node number 3605: 398 observations,    complexity param=5.258089e-05
##   predicted class=B1  expected loss=0.5477387  P(node) =0.00144831
##     class counts:   180   137    60    21     0
##    probabilities: 0.452 0.344 0.151 0.053 0.000 
##   left son=7210 (340 obs) right son=7211 (58 obs)
##   Primary splits:
##       reimbursement2008 < 4700   to the right, improve=5.7797840, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.1372610, (0 missing)
##       age               < 34.5   to the left,  improve=0.9964329, (0 missing)
##       bucket2008        < 2.5    to the right, improve=0.5848011, (0 missing)
##       kidney            < 0.5    to the right, improve=0.4151452, (0 missing)
## 
## Node number 3624: 329 observations
##   predicted class=B1  expected loss=0.4832827  P(node) =0.001197221
##     class counts:   170   105    35    16     3
##    probabilities: 0.517 0.319 0.106 0.049 0.009 
## 
## Node number 3625: 76 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.5394737  P(node) =0.0002765618
##     class counts:    32    35     7     2     0
##    probabilities: 0.421 0.461 0.092 0.026 0.000 
##   left son=7250 (21 obs) right son=7251 (55 obs)
##   Primary splits:
##       reimbursement2008 < 6785   to the right, improve=3.2066300, (0 missing)
##       bucket2008        < 2.5    to the right, improve=3.1159910, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.9967220, (0 missing)
##       age               < 85.5   to the right, improve=1.1176690, (0 missing)
##       kidney            < 0.5    to the right, improve=0.9258269, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.921, adj=0.714, (0 split)
##       kidney     < 0.5    to the right, agree=0.789, adj=0.238, (0 split)
## 
## Node number 3626: 362 observations
##   predicted class=B1  expected loss=0.5552486  P(node) =0.001317307
##     class counts:   161   128    47    25     1
##    probabilities: 0.445 0.354 0.130 0.069 0.003 
## 
## Node number 3627: 90 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.5  P(node) =0.0003275073
##     class counts:    29    45    11     5     0
##    probabilities: 0.322 0.500 0.122 0.056 0.000 
##   left son=7254 (21 obs) right son=7255 (69 obs)
##   Primary splits:
##       age               < 69.5   to the left,  improve=3.1544510, (0 missing)
##       alzheimers        < 0.5    to the right, improve=3.1535260, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.7000000, (0 missing)
##       reimbursement2008 < 3185   to the left,  improve=1.5133190, (0 missing)
##       copd              < 0.5    to the right, improve=0.3083333, (0 missing)
## 
## Node number 3654: 91 observations
##   predicted class=B1  expected loss=0.5054945  P(node) =0.0003311463
##     class counts:    45    31     9     6     0
##    probabilities: 0.495 0.341 0.099 0.066 0.000 
## 
## Node number 3655: 266 observations
##   predicted class=B2  expected loss=0.556391  P(node) =0.0009679661
##     class counts:    95   118    39    13     1
##    probabilities: 0.357 0.444 0.147 0.049 0.004 
## 
## Node number 3696: 105 observations
##   predicted class=B1  expected loss=0.5238095  P(node) =0.0003820919
##     class counts:    50    35    16     3     1
##    probabilities: 0.476 0.333 0.152 0.029 0.010 
## 
## Node number 3697: 27 observations
##   predicted class=B3  expected loss=0.6296296  P(node) =9.82522e-05
##     class counts:     4     7    10     6     0
##    probabilities: 0.148 0.259 0.370 0.222 0.000 
## 
## Node number 3850: 137 observations
##   predicted class=B1  expected loss=0.4963504  P(node) =0.000498539
##     class counts:    69    41    17     9     1
##    probabilities: 0.504 0.299 0.124 0.066 0.007 
## 
## Node number 3851: 372 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5913978  P(node) =0.001353697
##     class counts:   152   144    50    24     2
##    probabilities: 0.409 0.387 0.134 0.065 0.005 
##   left son=7702 (330 obs) right son=7703 (42 obs)
##   Primary splits:
##       reimbursement2008 < 4055   to the right, improve=3.5107950, (0 missing)
##       age               < 96     to the left,  improve=2.0766610, (0 missing)
##       depression        < 0.5    to the left,  improve=1.3295540, (0 missing)
##       copd              < 0.5    to the left,  improve=1.2612770, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.1950857, (0 missing)
## 
## Node number 3852: 211 observations,    complexity param=9.409212e-05
##   predicted class=B1  expected loss=0.563981  P(node) =0.0007678228
##     class counts:    92    89    23     7     0
##    probabilities: 0.436 0.422 0.109 0.033 0.000 
##   left son=7704 (142 obs) right son=7705 (69 obs)
##   Primary splits:
##       reimbursement2008 < 4075   to the left,  improve=3.4884480, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.9268848, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.8762896, (0 missing)
##       age               < 95     to the right, improve=0.6396384, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.3979516, (0 missing)
##   Surrogate splits:
##       age < 96.5   to the left,  agree=0.687, adj=0.043, (0 split)
## 
## Node number 3853: 128 observations
##   predicted class=B1  expected loss=0.546875  P(node) =0.0004657882
##     class counts:    58    38    22     9     1
##    probabilities: 0.453 0.297 0.172 0.070 0.008 
## 
## Node number 3854: 181 observations
##   predicted class=B2  expected loss=0.4861878  P(node) =0.0006586537
##     class counts:    56    93    23     7     2
##    probabilities: 0.309 0.514 0.127 0.039 0.011 
## 
## Node number 3855: 58 observations
##   predicted class=B3  expected loss=0.5862069  P(node) =0.0002110603
##     class counts:    14    16    24     4     0
##    probabilities: 0.241 0.276 0.414 0.069 0.000 
## 
## Node number 3858: 116 observations,    complexity param=6.088314e-05
##   predicted class=B2  expected loss=0.5517241  P(node) =0.0004221206
##     class counts:    41    52    14     7     2
##    probabilities: 0.353 0.448 0.121 0.060 0.017 
##   left son=7716 (63 obs) right son=7717 (53 obs)
##   Primary splits:
##       age               < 74.5   to the right, improve=2.8010500, (0 missing)
##       reimbursement2008 < 17265  to the left,  improve=2.4722090, (0 missing)
##       bucket2008        < 4.5    to the left,  improve=1.9776500, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.5892240, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.5845524, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 15590  to the right, agree=0.603, adj=0.132, (0 split)
##       heart.failure     < 0.5    to the right, agree=0.578, adj=0.075, (0 split)
##       alzheimers        < 0.5    to the right, agree=0.560, adj=0.038, (0 split)
##       osteoporosis      < 0.5    to the left,  agree=0.552, adj=0.019, (0 split)
##       bucket2008        < 3.5    to the right, agree=0.552, adj=0.019, (0 split)
## 
## Node number 3859: 449 observations
##   predicted class=B1  expected loss=0.6191537  P(node) =0.001633898
##     class counts:   171   127   110    34     7
##    probabilities: 0.381 0.283 0.245 0.076 0.016 
## 
## Node number 3860: 343 observations
##   predicted class=B1  expected loss=0.5014577  P(node) =0.001248167
##     class counts:   171   126    33    11     2
##    probabilities: 0.499 0.367 0.096 0.032 0.006 
## 
## Node number 3861: 1610 observations,    complexity param=9.962695e-05
##   predicted class=B2  expected loss=0.5925466  P(node) =0.005858742
##     class counts:   653   656   218    77     6
##    probabilities: 0.406 0.407 0.135 0.048 0.004 
##   left son=7722 (43 obs) right son=7723 (1567 obs)
##   Primary splits:
##       age               < 42.5   to the left,  improve=2.9787580, (0 missing)
##       reimbursement2008 < 3475   to the right, improve=1.6291410, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.4315089, (0 missing)
##       copd              < 0.5    to the left,  improve=0.2703192, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.2492479, (0 missing)
## 
## Node number 3862: 866 observations,    complexity param=0.0001605101
##   predicted class=B1  expected loss=0.6120092  P(node) =0.003151348
##     class counts:   336   322   139    64     5
##    probabilities: 0.388 0.372 0.161 0.074 0.006 
##   left son=7724 (129 obs) right son=7725 (737 obs)
##   Primary splits:
##       reimbursement2008 < 8115   to the right, improve=2.0175360, (0 missing)
##       age               < 89.5   to the left,  improve=1.8274460, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.6689370, (0 missing)
##       bucket2008        < 2.5    to the right, improve=1.3462440, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.5970317, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.984, adj=0.891, (0 split)
## 
## Node number 3863: 1381 observations,    complexity param=7.19528e-05
##   predicted class=B2  expected loss=0.5756698  P(node) =0.005025418
##     class counts:   459   586   244    84     8
##    probabilities: 0.332 0.424 0.177 0.061 0.006 
##   left son=7726 (997 obs) right son=7727 (384 obs)
##   Primary splits:
##       copd              < 0.5    to the left,  improve=3.5627620, (0 missing)
##       age               < 37.5   to the right, improve=2.2016010, (0 missing)
##       reimbursement2008 < 5195   to the left,  improve=1.9417980, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.9283600, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.3841814, (0 missing)
##   Surrogate splits:
##       age               < 34     to the right, agree=0.723, adj=0.005, (0 split)
##       reimbursement2008 < 5325   to the left,  agree=0.723, adj=0.005, (0 split)
## 
## Node number 3864: 1964 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.552444  P(node) =0.007146938
##     class counts:   656   879   309   111     9
##    probabilities: 0.334 0.448 0.157 0.057 0.005 
##   left son=7728 (22 obs) right son=7729 (1942 obs)
##   Primary splits:
##       reimbursement2008 < 3085   to the left,  improve=3.418849, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.308540, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=2.919418, (0 missing)
##       age               < 66.5   to the right, improve=1.961336, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.448295, (0 missing)
## 
## Node number 3865: 23 observations
##   predicted class=B3  expected loss=0.5217391  P(node) =8.369632e-05
##     class counts:     6     6    11     0     0
##    probabilities: 0.261 0.261 0.478 0.000 0.000 
## 
## Node number 3870: 837 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.6356033  P(node) =0.003045818
##     class counts:   292   305   155    82     3
##    probabilities: 0.349 0.364 0.185 0.098 0.004 
##   left son=7740 (639 obs) right son=7741 (198 obs)
##   Primary splits:
##       reimbursement2008 < 21320  to the left,  improve=1.8992410, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.6748470, (0 missing)
##       age               < 49.5   to the left,  improve=1.4981360, (0 missing)
##       bucket2008        < 3.5    to the left,  improve=1.4460210, (0 missing)
##       stroke            < 0.5    to the right, improve=0.7571134, (0 missing)
##   Surrogate splits:
##       bucket2008 < 3.5    to the left,  agree=0.955, adj=0.808, (0 split)
## 
## Node number 3871: 591 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.607445  P(node) =0.002150632
##     class counts:   163   232   127    64     5
##    probabilities: 0.276 0.393 0.215 0.108 0.008 
##   left son=7742 (122 obs) right son=7743 (469 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the left,  improve=3.4607800, (0 missing)
##       reimbursement2008 < 8775   to the left,  improve=1.9199300, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.2822570, (0 missing)
##       copd              < 0.5    to the left,  improve=1.1325150, (0 missing)
##       age               < 80.5   to the right, improve=0.6677683, (0 missing)
## 
## Node number 3970: 346 observations
##   predicted class=B1  expected loss=0.5375723  P(node) =0.001259084
##     class counts:   160    92    52    37     5
##    probabilities: 0.462 0.266 0.150 0.107 0.014 
## 
## Node number 3971: 125 observations,    complexity param=5.811572e-05
##   predicted class=B2  expected loss=0.656  P(node) =0.0004548713
##     class counts:    41    43    25    16     0
##    probabilities: 0.328 0.344 0.200 0.128 0.000 
##   left son=7942 (106 obs) right son=7943 (19 obs)
##   Primary splits:
##       age               < 62     to the right, improve=3.3415930, (0 missing)
##       reimbursement2008 < 11475  to the left,  improve=2.2730020, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.7920000, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.7319750, (0 missing)
##       stroke            < 0.5    to the right, improve=0.5402967, (0 missing)
## 
## Node number 3980: 1234 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.6612642  P(node) =0.00449049
##     class counts:   374   418   252   160    30
##    probabilities: 0.303 0.339 0.204 0.130 0.024 
##   left son=7960 (349 obs) right son=7961 (885 obs)
##   Primary splits:
##       reimbursement2008 < 12135  to the right, improve=3.745241, (0 missing)
##       age               < 67.5   to the left,  improve=3.421516, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.338981, (0 missing)
##       bucket2008        < 2.5    to the right, improve=1.254047, (0 missing)
##       copd              < 0.5    to the right, improve=1.093433, (0 missing)
## 
## Node number 3981: 1190 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.6537815  P(node) =0.004330375
##     class counts:   289   412   295   175    19
##    probabilities: 0.243 0.346 0.248 0.147 0.016 
##   left son=7962 (547 obs) right son=7963 (643 obs)
##   Primary splits:
##       copd              < 0.5    to the left,  improve=2.655589, (0 missing)
##       age               < 82.5   to the right, improve=1.853724, (0 missing)
##       stroke            < 0.5    to the right, improve=1.583996, (0 missing)
##       reimbursement2008 < 6355   to the right, improve=1.325969, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.085887, (0 missing)
##   Surrogate splits:
##       heart.failure     < 0.5    to the left,  agree=0.562, adj=0.048, (0 split)
##       reimbursement2008 < 7315   to the left,  agree=0.555, adj=0.031, (0 split)
##       alzheimers        < 0.5    to the left,  agree=0.543, adj=0.005, (0 split)
##       age               < 28.5   to the left,  agree=0.542, adj=0.004, (0 split)
## 
## Node number 4060: 62 observations
##   predicted class=B2  expected loss=0.5806452  P(node) =0.0002256162
##     class counts:     3    26    17    15     1
##    probabilities: 0.048 0.419 0.274 0.242 0.016 
## 
## Node number 4061: 175 observations
##   predicted class=B4  expected loss=0.5885714  P(node) =0.0006368198
##     class counts:    10    50    32    72    11
##    probabilities: 0.057 0.286 0.183 0.411 0.063 
## 
## Node number 4068: 172 observations
##   predicted class=B2  expected loss=0.6511628  P(node) =0.0006259029
##     class counts:    27    60    35    39    11
##    probabilities: 0.157 0.349 0.203 0.227 0.064 
## 
## Node number 4069: 19 observations
##   predicted class=B3  expected loss=0.5263158  P(node) =6.914044e-05
##     class counts:     2     2     9     3     3
##    probabilities: 0.105 0.105 0.474 0.158 0.158 
## 
## Node number 4070: 468 observations,    complexity param=7.379774e-05
##   predicted class=B1  expected loss=0.7200855  P(node) =0.001703038
##     class counts:   131   112    77   122    26
##    probabilities: 0.280 0.239 0.165 0.261 0.056 
##   left son=8140 (457 obs) right son=8141 (11 obs)
##   Primary splits:
##       age               < 93.5   to the left,  improve=1.955850, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=1.902288, (0 missing)
##       reimbursement2008 < 19700  to the right, improve=1.873153, (0 missing)
##       ihd               < 0.5    to the right, improve=1.868954, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.716285, (0 missing)
## 
## Node number 4071: 513 observations
##   predicted class=B4  expected loss=0.6237817  P(node) =0.001866792
##     class counts:   112    91    81   193    36
##    probabilities: 0.218 0.177 0.158 0.376 0.070 
## 
## Node number 6608: 251 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.4701195  P(node) =0.0009133816
##     class counts:   133    73    33    11     1
##    probabilities: 0.530 0.291 0.131 0.044 0.004 
##   left son=13216 (235 obs) right son=13217 (16 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=2.6866090, (0 missing)
##       ihd               < 0.5    to the left,  improve=2.4584220, (0 missing)
##       reimbursement2008 < 1815   to the left,  improve=1.2636760, (0 missing)
##       age               < 60.5   to the right, improve=1.0616730, (0 missing)
##       depression        < 0.5    to the left,  improve=0.7871996, (0 missing)
## 
## Node number 6609: 14 observations
##   predicted class=B2  expected loss=0.3571429  P(node) =5.094559e-05
##     class counts:     2     9     1     2     0
##    probabilities: 0.143 0.643 0.071 0.143 0.000 
## 
## Node number 6610: 213 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5633803  P(node) =0.0007751007
##     class counts:    93    74    30    15     1
##    probabilities: 0.437 0.347 0.141 0.070 0.005 
##   left son=13220 (201 obs) right son=13221 (12 obs)
##   Primary splits:
##       age               < 44.5   to the right, improve=1.7572700, (0 missing)
##       reimbursement2008 < 2005   to the right, improve=1.0657280, (0 missing)
##       cancer            < 0.5    to the right, improve=0.3892571, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.3640621, (0 missing)
##       depression        < 0.5    to the right, improve=0.3467310, (0 missing)
## 
## Node number 6611: 77 observations
##   predicted class=B2  expected loss=0.5194805  P(node) =0.0002802007
##     class counts:    25    37    12     1     2
##    probabilities: 0.325 0.481 0.156 0.013 0.026 
## 
## Node number 6940: 71 observations
##   predicted class=B1  expected loss=0.4366197  P(node) =0.0002583669
##     class counts:    40    16    11     3     1
##    probabilities: 0.563 0.225 0.155 0.042 0.014 
## 
## Node number 6941: 769 observations,    complexity param=6.088314e-05
##   predicted class=B1  expected loss=0.5552666  P(node) =0.002798368
##     class counts:   342   293    92    38     4
##    probabilities: 0.445 0.381 0.120 0.049 0.005 
##   left son=13882 (472 obs) right son=13883 (297 obs)
##   Primary splits:
##       age               < 70.5   to the right, improve=2.5248320, (0 missing)
##       depression        < 0.5    to the left,  improve=1.8557100, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.7236880, (0 missing)
##       reimbursement2008 < 2665   to the right, improve=1.1252400, (0 missing)
##       copd              < 0.5    to the left,  improve=0.9387137, (0 missing)
## 
## Node number 7000: 1074 observations,    complexity param=7.19528e-05
##   predicted class=B1  expected loss=0.5772812  P(node) =0.003908254
##     class counts:   454   373   166    74     7
##    probabilities: 0.423 0.347 0.155 0.069 0.007 
##   left son=14000 (808 obs) right son=14001 (266 obs)
##   Primary splits:
##       copd              < 0.5    to the left,  improve=2.7468870, (0 missing)
##       cancer            < 0.5    to the left,  improve=1.8315690, (0 missing)
##       age               < 78.5   to the left,  improve=1.6074350, (0 missing)
##       reimbursement2008 < 2575   to the right, improve=1.2651380, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.9951466, (0 missing)
## 
## Node number 7001: 25 observations
##   predicted class=B2  expected loss=0.4  P(node) =9.097426e-05
##     class counts:     6    15     2     2     0
##    probabilities: 0.240 0.600 0.080 0.080 0.000 
## 
## Node number 7002: 303 observations
##   predicted class=B1  expected loss=0.6039604  P(node) =0.001102608
##     class counts:   120    99    62    21     1
##    probabilities: 0.396 0.327 0.205 0.069 0.003 
## 
## Node number 7003: 350 observations
##   predicted class=B2  expected loss=0.5942857  P(node) =0.00127364
##     class counts:   106   142    64    34     4
##    probabilities: 0.303 0.406 0.183 0.097 0.011 
## 
## Node number 7016: 229 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5938865  P(node) =0.0008333242
##     class counts:    93    82    44     8     2
##    probabilities: 0.406 0.358 0.192 0.035 0.009 
##   left son=14032 (15 obs) right son=14033 (214 obs)
##   Primary splits:
##       cancer            < 0.5    to the right, improve=1.9222650, (0 missing)
##       reimbursement2008 < 2515   to the left,  improve=1.4164780, (0 missing)
##       age               < 94     to the left,  improve=1.2547820, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.6631197, (0 missing)
##       copd              < 0.5    to the right, improve=0.2469242, (0 missing)
## 
## Node number 7017: 153 observations,    complexity param=6.272808e-05
##   predicted class=B2  expected loss=0.5947712  P(node) =0.0005567625
##     class counts:    49    62    30    10     2
##    probabilities: 0.320 0.405 0.196 0.065 0.013 
##   left son=14034 (14 obs) right son=14035 (139 obs)
##   Primary splits:
##       reimbursement2008 < 2545   to the right, improve=2.7113570, (0 missing)
##       age               < 45     to the left,  improve=1.6972360, (0 missing)
##       cancer            < 0.5    to the left,  improve=0.6348039, (0 missing)
##       copd              < 0.5    to the right, improve=0.3887797, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.2839287, (0 missing)
## 
## Node number 7088: 275 observations,    complexity param=7.748763e-05
##   predicted class=B2  expected loss=0.56  P(node) =0.001000717
##     class counts:   108   121    34    11     1
##    probabilities: 0.393 0.440 0.124 0.040 0.004 
##   left son=14176 (44 obs) right son=14177 (231 obs)
##   Primary splits:
##       age               < 63.5   to the left,  improve=2.2068400, (0 missing)
##       reimbursement2008 < 2555   to the right, improve=1.7374730, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.5968660, (0 missing)
##       copd              < 0.5    to the left,  improve=0.9046397, (0 missing)
##       heart.failure     < 0.5    to the right, improve=0.5279104, (0 missing)
## 
## Node number 7089: 733 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5770805  P(node) =0.002667365
##     class counts:   310   279    99    42     3
##    probabilities: 0.423 0.381 0.135 0.057 0.004 
##   left son=14178 (10 obs) right son=14179 (723 obs)
##   Primary splits:
##       age               < 97.5   to the right, improve=1.1550490, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.1107930, (0 missing)
##       reimbursement2008 < 2495   to the right, improve=0.7495829, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.7242328, (0 missing)
##       depression        < 0.5    to the left,  improve=0.5684301, (0 missing)
## 
## Node number 7210: 340 observations
##   predicted class=B1  expected loss=0.5088235  P(node) =0.00123725
##     class counts:   167   107    48    18     0
##    probabilities: 0.491 0.315 0.141 0.053 0.000 
## 
## Node number 7211: 58 observations
##   predicted class=B2  expected loss=0.4827586  P(node) =0.0002110603
##     class counts:    13    30    12     3     0
##    probabilities: 0.224 0.517 0.207 0.052 0.000 
## 
## Node number 7250: 21 observations
##   predicted class=B1  expected loss=0.3333333  P(node) =7.641838e-05
##     class counts:    14     5     2     0     0
##    probabilities: 0.667 0.238 0.095 0.000 0.000 
## 
## Node number 7251: 55 observations
##   predicted class=B2  expected loss=0.4545455  P(node) =0.0002001434
##     class counts:    18    30     5     2     0
##    probabilities: 0.327 0.545 0.091 0.036 0.000 
## 
## Node number 7254: 21 observations
##   predicted class=B1  expected loss=0.4285714  P(node) =7.641838e-05
##     class counts:    12     6     1     2     0
##    probabilities: 0.571 0.286 0.048 0.095 0.000 
## 
## Node number 7255: 69 observations
##   predicted class=B2  expected loss=0.4347826  P(node) =0.000251089
##     class counts:    17    39    10     3     0
##    probabilities: 0.246 0.565 0.145 0.043 0.000 
## 
## Node number 7702: 330 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.569697  P(node) =0.00120086
##     class counts:   142   119    44    23     2
##    probabilities: 0.430 0.361 0.133 0.070 0.006 
##   left son=15404 (309 obs) right son=15405 (21 obs)
##   Primary splits:
##       reimbursement2008 < 4185   to the right, improve=2.2681710, (0 missing)
##       age               < 96     to the left,  improve=2.1333520, (0 missing)
##       depression        < 0.5    to the left,  improve=0.7533962, (0 missing)
##       copd              < 0.5    to the left,  improve=0.6700147, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.3769465, (0 missing)
## 
## Node number 7703: 42 observations
##   predicted class=B2  expected loss=0.4047619  P(node) =0.0001528368
##     class counts:    10    25     6     1     0
##    probabilities: 0.238 0.595 0.143 0.024 0.000 
## 
## Node number 7704: 142 observations
##   predicted class=B1  expected loss=0.5  P(node) =0.0005167338
##     class counts:    71    51    15     5     0
##    probabilities: 0.500 0.359 0.106 0.035 0.000 
## 
## Node number 7705: 69 observations
##   predicted class=B2  expected loss=0.4492754  P(node) =0.000251089
##     class counts:    21    38     8     2     0
##    probabilities: 0.304 0.551 0.116 0.029 0.000 
## 
## Node number 7716: 63 observations
##   predicted class=B1  expected loss=0.5714286  P(node) =0.0002292551
##     class counts:    27    21    10     4     1
##    probabilities: 0.429 0.333 0.159 0.063 0.016 
## 
## Node number 7717: 53 observations
##   predicted class=B2  expected loss=0.4150943  P(node) =0.0001928654
##     class counts:    14    31     4     3     1
##    probabilities: 0.264 0.585 0.075 0.057 0.019 
## 
## Node number 7722: 43 observations
##   predicted class=B1  expected loss=0.3953488  P(node) =0.0001564757
##     class counts:    26    11     3     3     0
##    probabilities: 0.605 0.256 0.070 0.070 0.000 
## 
## Node number 7723: 1567 observations,    complexity param=9.962695e-05
##   predicted class=B2  expected loss=0.5883854  P(node) =0.005702267
##     class counts:   627   645   215    74     6
##    probabilities: 0.400 0.412 0.137 0.047 0.004 
##   left son=15446 (1527 obs) right son=15447 (40 obs)
##   Primary splits:
##       age               < 50.5   to the right, improve=1.7032880, (0 missing)
##       reimbursement2008 < 3475   to the right, improve=1.5459000, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.4552758, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.2471234, (0 missing)
##       copd              < 0.5    to the left,  improve=0.2014160, (0 missing)
## 
## Node number 7724: 129 observations
##   predicted class=B2  expected loss=0.5271318  P(node) =0.0004694272
##     class counts:    46    61    16     6     0
##    probabilities: 0.357 0.473 0.124 0.047 0.000 
## 
## Node number 7725: 737 observations,    complexity param=9.962695e-05
##   predicted class=B1  expected loss=0.6065129  P(node) =0.002681921
##     class counts:   290   261   123    58     5
##    probabilities: 0.393 0.354 0.167 0.079 0.007 
##   left son=15450 (703 obs) right son=15451 (34 obs)
##   Primary splits:
##       age               < 94.5   to the left,  improve=1.9050170, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.5567680, (0 missing)
##       reimbursement2008 < 6575   to the right, improve=1.5078350, (0 missing)
##       copd              < 0.5    to the left,  improve=0.5423379, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.5213862, (0 missing)
## 
## Node number 7726: 997 observations,    complexity param=7.19528e-05
##   predicted class=B2  expected loss=0.5927783  P(node) =0.003628054
##     class counts:   357   406   171    57     6
##    probabilities: 0.358 0.407 0.172 0.057 0.006 
##   left son=15452 (297 obs) right son=15453 (700 obs)
##   Primary splits:
##       age               < 69.5   to the left,  improve=2.4458440, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=2.2624190, (0 missing)
##       reimbursement2008 < 4135   to the right, improve=1.8635870, (0 missing)
##       stroke            < 0.5    to the right, improve=0.3191114, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.3114115, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 5325   to the right, agree=0.703, adj=0.003, (0 split)
## 
## Node number 7727: 384 observations
##   predicted class=B2  expected loss=0.53125  P(node) =0.001397365
##     class counts:   102   180    73    27     2
##    probabilities: 0.266 0.469 0.190 0.070 0.005 
## 
## Node number 7728: 22 observations
##   predicted class=B1  expected loss=0.3636364  P(node) =8.005735e-05
##     class counts:    14     5     1     2     0
##    probabilities: 0.636 0.227 0.045 0.091 0.000 
## 
## Node number 7729: 1942 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5499485  P(node) =0.007066881
##     class counts:   642   874   308   109     9
##    probabilities: 0.331 0.450 0.159 0.056 0.005 
##   left son=15458 (889 obs) right son=15459 (1053 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the left,  improve=3.215424, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=2.693064, (0 missing)
##       reimbursement2008 < 8025   to the left,  improve=2.054172, (0 missing)
##       age               < 66.5   to the right, improve=1.953237, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.238773, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 3815   to the left,  agree=0.545, adj=0.007, (0 split)
##       age               < 31.5   to the left,  agree=0.544, adj=0.003, (0 split)
## 
## Node number 7740: 639 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.6353678  P(node) =0.002325302
##     class counts:   233   221   124    59     2
##    probabilities: 0.365 0.346 0.194 0.092 0.003 
##   left son=15480 (83 obs) right son=15481 (556 obs)
##   Primary splits:
##       age               < 49.5   to the left,  improve=1.7453130, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.0790130, (0 missing)
##       reimbursement2008 < 10445  to the right, improve=1.0644190, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.9731198, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.6917546, (0 missing)
## 
## Node number 7741: 198 observations
##   predicted class=B2  expected loss=0.5757576  P(node) =0.0007205162
##     class counts:    59    84    31    23     1
##    probabilities: 0.298 0.424 0.157 0.116 0.005 
## 
## Node number 7742: 122 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5983607  P(node) =0.0004439544
##     class counts:    49    39    22    12     0
##    probabilities: 0.402 0.320 0.180 0.098 0.000 
##   left son=15484 (40 obs) right son=15485 (82 obs)
##   Primary splits:
##       reimbursement2008 < 11560  to the left,  improve=2.7817470, (0 missing)
##       age               < 80.5   to the right, improve=1.9103730, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.4632510, (0 missing)
##       copd              < 0.5    to the left,  improve=1.0641520, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.9890302, (0 missing)
## 
## Node number 7743: 469 observations
##   predicted class=B2  expected loss=0.5884861  P(node) =0.001706677
##     class counts:   114   193   105    52     5
##    probabilities: 0.243 0.412 0.224 0.111 0.011 
## 
## Node number 7942: 106 observations,    complexity param=5.811572e-05
##   predicted class=B1  expected loss=0.6320755  P(node) =0.0003857309
##     class counts:    39    39    16    12     0
##    probabilities: 0.368 0.368 0.151 0.113 0.000 
##   left son=15884 (93 obs) right son=15885 (13 obs)
##   Primary splits:
##       age               < 67.5   to the right, improve=2.3273090, (0 missing)
##       reimbursement2008 < 11575  to the left,  improve=1.8244140, (0 missing)
##       stroke            < 0.5    to the right, improve=0.5034792, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.4237564, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.3937905, (0 missing)
## 
## Node number 7943: 19 observations
##   predicted class=B3  expected loss=0.5263158  P(node) =6.914044e-05
##     class counts:     2     4     9     4     0
##    probabilities: 0.105 0.211 0.474 0.211 0.000 
## 
## Node number 7960: 349 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.6475645  P(node) =0.001270001
##     class counts:   123   103    55    57    11
##    probabilities: 0.352 0.295 0.158 0.163 0.032 
##   left son=15920 (331 obs) right son=15921 (18 obs)
##   Primary splits:
##       age               < 54     to the right, improve=2.0196730, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.9958000, (0 missing)
##       reimbursement2008 < 15235  to the left,  improve=1.7314800, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.9381122, (0 missing)
##       copd              < 0.5    to the right, improve=0.4818854, (0 missing)
## 
## Node number 7961: 885 observations
##   predicted class=B2  expected loss=0.6440678  P(node) =0.003220489
##     class counts:   251   315   197   103    19
##    probabilities: 0.284 0.356 0.223 0.116 0.021 
## 
## Node number 7962: 547 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.6709324  P(node) =0.001990517
##     class counts:   154   180   136    65    12
##    probabilities: 0.282 0.329 0.249 0.119 0.022 
##   left son=15924 (310 obs) right son=15925 (237 obs)
##   Primary splits:
##       reimbursement2008 < 9205   to the right, improve=2.7787810, (0 missing)
##       age               < 68.5   to the right, improve=2.5123800, (0 missing)
##       bucket2008        < 2.5    to the right, improve=0.9624740, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.6055315, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.5192530, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.835, adj=0.620, (0 split)
##       age        < 44.5   to the right, agree=0.581, adj=0.034, (0 split)
## 
## Node number 7963: 643 observations
##   predicted class=B2  expected loss=0.6391913  P(node) =0.002339858
##     class counts:   135   232   159   110     7
##    probabilities: 0.210 0.361 0.247 0.171 0.011 
## 
## Node number 8140: 457 observations,    complexity param=7.379774e-05
##   predicted class=B1  expected loss=0.7133479  P(node) =0.00166301
##     class counts:   131   107    73   120    26
##    probabilities: 0.287 0.234 0.160 0.263 0.057 
##   left son=16280 (398 obs) right son=16281 (59 obs)
##   Primary splits:
##       age               < 86.5   to the left,  improve=2.218362, (0 missing)
##       ihd               < 0.5    to the right, improve=2.044330, (0 missing)
##       reimbursement2008 < 19430  to the right, improve=1.853412, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.735004, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=1.352826, (0 missing)
## 
## Node number 8141: 11 observations
##   predicted class=B2  expected loss=0.5454545  P(node) =4.002868e-05
##     class counts:     0     5     4     2     0
##    probabilities: 0.000 0.455 0.364 0.182 0.000 
## 
## Node number 13216: 235 observations
##   predicted class=B1  expected loss=0.4510638  P(node) =0.0008551581
##     class counts:   129    64    30    11     1
##    probabilities: 0.549 0.272 0.128 0.047 0.004 
## 
## Node number 13217: 16 observations
##   predicted class=B2  expected loss=0.4375  P(node) =5.822353e-05
##     class counts:     4     9     3     0     0
##    probabilities: 0.250 0.562 0.188 0.000 0.000 
## 
## Node number 13220: 201 observations
##   predicted class=B1  expected loss=0.5472637  P(node) =0.0007314331
##     class counts:    91    67    29    14     0
##    probabilities: 0.453 0.333 0.144 0.070 0.000 
## 
## Node number 13221: 12 observations
##   predicted class=B2  expected loss=0.4166667  P(node) =4.366765e-05
##     class counts:     2     7     1     1     1
##    probabilities: 0.167 0.583 0.083 0.083 0.083 
## 
## Node number 13882: 472 observations,    complexity param=5.165842e-05
##   predicted class=B1  expected loss=0.5275424  P(node) =0.001717594
##     class counts:   223   163    57    25     4
##    probabilities: 0.472 0.345 0.121 0.053 0.008 
##   left son=27764 (343 obs) right son=27765 (129 obs)
##   Primary splits:
##       age               < 73.5   to the right, improve=4.630612, (0 missing)
##       reimbursement2008 < 2805   to the right, improve=1.597068, (0 missing)
##       depression        < 0.5    to the left,  improve=1.459900, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.335760, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.130037, (0 missing)
## 
## Node number 13883: 297 observations,    complexity param=6.088314e-05
##   predicted class=B2  expected loss=0.5622896  P(node) =0.001080774
##     class counts:   119   130    35    13     0
##    probabilities: 0.401 0.438 0.118 0.044 0.000 
##   left son=27766 (218 obs) right son=27767 (79 obs)
##   Primary splits:
##       osteoporosis      < 0.5    to the left,  improve=1.3951400, (0 missing)
##       reimbursement2008 < 2945   to the right, improve=1.0350230, (0 missing)
##       depression        < 0.5    to the left,  improve=0.9259259, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.7583938, (0 missing)
##       copd              < 0.5    to the left,  improve=0.3569379, (0 missing)
## 
## Node number 14000: 808 observations
##   predicted class=B1  expected loss=0.5569307  P(node) =0.002940288
##     class counts:   358   273   111    61     5
##    probabilities: 0.443 0.338 0.137 0.075 0.006 
## 
## Node number 14001: 266 observations,    complexity param=7.19528e-05
##   predicted class=B2  expected loss=0.6240602  P(node) =0.0009679661
##     class counts:    96   100    55    13     2
##    probabilities: 0.361 0.376 0.207 0.049 0.008 
##   left son=28002 (192 obs) right son=28003 (74 obs)
##   Primary splits:
##       reimbursement2008 < 2540   to the right, improve=2.9691060, (0 missing)
##       age               < 78.5   to the left,  improve=2.6852920, (0 missing)
##       cancer            < 0.5    to the right, improve=2.3754980, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.7018574, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.6157537, (0 missing)
##   Surrogate splits:
##       age < 50.5   to the right, agree=0.737, adj=0.054, (0 split)
## 
## Node number 14032: 15 observations
##   predicted class=B1  expected loss=0.3333333  P(node) =5.458456e-05
##     class counts:    10     2     3     0     0
##    probabilities: 0.667 0.133 0.200 0.000 0.000 
## 
## Node number 14033: 214 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.6121495  P(node) =0.0007787397
##     class counts:    83    80    41     8     2
##    probabilities: 0.388 0.374 0.192 0.037 0.009 
##   left son=28066 (169 obs) right son=28067 (45 obs)
##   Primary splits:
##       reimbursement2008 < 2515   to the left,  improve=1.6030020, (0 missing)
##       age               < 52.5   to the right, improve=0.9765448, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.7668533, (0 missing)
##       copd              < 0.5    to the right, improve=0.3681910, (0 missing)
##       ihd               < 0.5    to the right, improve=0.1207875, (0 missing)
## 
## Node number 14034: 14 observations
##   predicted class=B1  expected loss=0.3571429  P(node) =5.094559e-05
##     class counts:     9     2     2     1     0
##    probabilities: 0.643 0.143 0.143 0.071 0.000 
## 
## Node number 14035: 139 observations
##   predicted class=B2  expected loss=0.5683453  P(node) =0.0005058169
##     class counts:    40    60    28     9     2
##    probabilities: 0.288 0.432 0.201 0.065 0.014 
## 
## Node number 14176: 44 observations
##   predicted class=B2  expected loss=0.3863636  P(node) =0.0001601147
##     class counts:    14    27     2     1     0
##    probabilities: 0.318 0.614 0.045 0.023 0.000 
## 
## Node number 14177: 231 observations,    complexity param=7.748763e-05
##   predicted class=B1  expected loss=0.5930736  P(node) =0.0008406022
##     class counts:    94    94    32    10     1
##    probabilities: 0.407 0.407 0.139 0.043 0.004 
##   left son=28354 (169 obs) right son=28355 (62 obs)
##   Primary splits:
##       alzheimers        < 0.5    to the left,  improve=2.4583990, (0 missing)
##       reimbursement2008 < 2555   to the right, improve=1.0376560, (0 missing)
##       age               < 84.5   to the left,  improve=1.0243680, (0 missing)
##       copd              < 0.5    to the left,  improve=0.7240171, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.4410819, (0 missing)
##   Surrogate splits:
##       age < 87.5   to the left,  agree=0.745, adj=0.048, (0 split)
## 
## Node number 14178: 10 observations
##   predicted class=B1  expected loss=0.3  P(node) =3.63897e-05
##     class counts:     7     2     1     0     0
##    probabilities: 0.700 0.200 0.100 0.000 0.000 
## 
## Node number 14179: 723 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5809129  P(node) =0.002630976
##     class counts:   303   277    98    42     3
##    probabilities: 0.419 0.383 0.136 0.058 0.004 
##   left son=28358 (689 obs) right son=28359 (34 obs)
##   Primary splits:
##       age               < 90.5   to the left,  improve=1.6650270, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.5078050, (0 missing)
##       reimbursement2008 < 2495   to the right, improve=0.8133392, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.6699213, (0 missing)
##       depression        < 0.5    to the left,  improve=0.5296598, (0 missing)
## 
## Node number 15404: 309 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5728155  P(node) =0.001124442
##     class counts:   132   117    38    20     2
##    probabilities: 0.427 0.379 0.123 0.065 0.006 
##   left son=30808 (253 obs) right son=30809 (56 obs)
##   Primary splits:
##       reimbursement2008 < 4635   to the right, improve=2.0908250, (0 missing)
##       age               < 73.5   to the right, improve=1.8355900, (0 missing)
##       depression        < 0.5    to the left,  improve=0.6554201, (0 missing)
##       copd              < 0.5    to the left,  improve=0.3380891, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.2757170, (0 missing)
## 
## Node number 15405: 21 observations
##   predicted class=B1  expected loss=0.5238095  P(node) =7.641838e-05
##     class counts:    10     2     6     3     0
##    probabilities: 0.476 0.095 0.286 0.143 0.000 
## 
## Node number 15446: 1527 observations,    complexity param=9.962695e-05
##   predicted class=B2  expected loss=0.5854617  P(node) =0.005556708
##     class counts:   613   633   203    72     6
##    probabilities: 0.401 0.415 0.133 0.047 0.004 
##   left son=30892 (1478 obs) right son=30893 (49 obs)
##   Primary splits:
##       reimbursement2008 < 3465   to the right, improve=1.7561930, (0 missing)
##       age               < 59.5   to the right, improve=1.2446620, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.3864080, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.3262151, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.1237742, (0 missing)
## 
## Node number 15447: 40 observations
##   predicted class=B1  expected loss=0.65  P(node) =0.0001455588
##     class counts:    14    12    12     2     0
##    probabilities: 0.350 0.300 0.300 0.050 0.000 
## 
## Node number 15450: 703 observations,    complexity param=8.855729e-05
##   predicted class=B1  expected loss=0.598862  P(node) =0.002558196
##     class counts:   282   244   119    53     5
##    probabilities: 0.401 0.347 0.169 0.075 0.007 
##   left son=30900 (298 obs) right son=30901 (405 obs)
##   Primary splits:
##       reimbursement2008 < 6635   to the right, improve=1.9072210, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.4867600, (0 missing)
##       age               < 74.5   to the left,  improve=1.1374550, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.4608058, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.4586126, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.596, adj=0.047, (0 split)
##       age        < 35.5   to the left,  agree=0.578, adj=0.003, (0 split)
## 
## Node number 15451: 34 observations
##   predicted class=B2  expected loss=0.5  P(node) =0.000123725
##     class counts:     8    17     4     5     0
##    probabilities: 0.235 0.500 0.118 0.147 0.000 
## 
## Node number 15452: 297 observations,    complexity param=7.19528e-05
##   predicted class=B1  expected loss=0.5757576  P(node) =0.001080774
##     class counts:   126   113    45    12     1
##    probabilities: 0.424 0.380 0.152 0.040 0.003 
##   left son=30904 (274 obs) right son=30905 (23 obs)
##   Primary splits:
##       reimbursement2008 < 5065   to the left,  improve=2.3768610, (0 missing)
##       alzheimers        < 0.5    to the right, improve=2.2936150, (0 missing)
##       age               < 37.5   to the left,  improve=1.9456180, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=0.7719678, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.6098027, (0 missing)
## 
## Node number 15453: 700 observations
##   predicted class=B2  expected loss=0.5814286  P(node) =0.002547279
##     class counts:   231   293   126    45     5
##    probabilities: 0.330 0.419 0.180 0.064 0.007 
## 
## Node number 15458: 889 observations
##   predicted class=B2  expected loss=0.5714286  P(node) =0.003235045
##     class counts:   327   381   133    45     3
##    probabilities: 0.368 0.429 0.150 0.051 0.003 
## 
## Node number 15459: 1053 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5318139  P(node) =0.003831836
##     class counts:   315   493   175    64     6
##    probabilities: 0.299 0.468 0.166 0.061 0.006 
##   left son=30918 (721 obs) right son=30919 (332 obs)
##   Primary splits:
##       osteoporosis      < 0.5    to the left,  improve=2.319421, (0 missing)
##       age               < 65.5   to the right, improve=2.157808, (0 missing)
##       reimbursement2008 < 4195   to the left,  improve=2.005955, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.694776, (0 missing)
##       bucket2008        < 2.5    to the right, improve=0.685891, (0 missing)
## 
## Node number 15480: 83 observations
##   predicted class=B2  expected loss=0.5662651  P(node) =0.0003020345
##     class counts:    29    36     8    10     0
##    probabilities: 0.349 0.434 0.096 0.120 0.000 
## 
## Node number 15481: 556 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.6330935  P(node) =0.002023268
##     class counts:   204   185   116    49     2
##    probabilities: 0.367 0.333 0.209 0.088 0.004 
##   left son=30962 (368 obs) right son=30963 (188 obs)
##   Primary splits:
##       age               < 67.5   to the right, improve=1.7538220, (0 missing)
##       reimbursement2008 < 17290  to the right, improve=1.5233210, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.8892958, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.8663588, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=0.8033839, (0 missing)
## 
## Node number 15484: 40 observations
##   predicted class=B1  expected loss=0.425  P(node) =0.0001455588
##     class counts:    23     8     7     2     0
##    probabilities: 0.575 0.200 0.175 0.050 0.000 
## 
## Node number 15485: 82 observations
##   predicted class=B2  expected loss=0.6219512  P(node) =0.0002983956
##     class counts:    26    31    15    10     0
##    probabilities: 0.317 0.378 0.183 0.122 0.000 
## 
## Node number 15884: 93 observations,    complexity param=5.811572e-05
##   predicted class=B2  expected loss=0.5913978  P(node) =0.0003384243
##     class counts:    32    38    15     8     0
##    probabilities: 0.344 0.409 0.161 0.086 0.000 
##   left son=31768 (44 obs) right son=31769 (49 obs)
##   Primary splits:
##       reimbursement2008 < 6110   to the right, improve=2.8038180, (0 missing)
##       age               < 68.5   to the right, improve=0.9063337, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.4118188, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.3578690, (0 missing)
##       stroke            < 0.5    to the right, improve=0.3151562, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.785, adj=0.545, (0 split)
##       age        < 73.5   to the left,  agree=0.602, adj=0.159, (0 split)
##       alzheimers < 0.5    to the right, agree=0.538, adj=0.023, (0 split)
## 
## Node number 15885: 13 observations
##   predicted class=B1  expected loss=0.4615385  P(node) =4.730662e-05
##     class counts:     7     1     1     4     0
##    probabilities: 0.538 0.077 0.077 0.308 0.000 
## 
## Node number 15920: 331 observations
##   predicted class=B1  expected loss=0.6344411  P(node) =0.001204499
##     class counts:   121    94    53    53    10
##    probabilities: 0.366 0.284 0.160 0.160 0.030 
## 
## Node number 15921: 18 observations
##   predicted class=B2  expected loss=0.5  P(node) =6.550147e-05
##     class counts:     2     9     2     4     1
##    probabilities: 0.111 0.500 0.111 0.222 0.056 
## 
## Node number 15924: 310 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.6741935  P(node) =0.001128081
##     class counts:   101    99    64    37     9
##    probabilities: 0.326 0.319 0.206 0.119 0.029 
##   left son=31848 (50 obs) right son=31849 (260 obs)
##   Primary splits:
##       reimbursement2008 < 9955   to the left,  improve=3.5194040, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.4052180, (0 missing)
##       age               < 60.5   to the right, improve=1.3545900, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.9417634, (0 missing)
##       stroke            < 0.5    to the right, improve=0.4401818, (0 missing)
## 
## Node number 15925: 237 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.6582278  P(node) =0.000862436
##     class counts:    53    81    72    28     3
##    probabilities: 0.224 0.342 0.304 0.118 0.013 
##   left son=31850 (56 obs) right son=31851 (181 obs)
##   Primary splits:
##       age               < 67.5   to the left,  improve=3.14488400, (0 missing)
##       reimbursement2008 < 7130   to the left,  improve=2.11196700, (0 missing)
##       bucket2008        < 2.5    to the right, improve=0.86604090, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.39390990, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.05008339, (0 missing)
## 
## Node number 16280: 398 observations,    complexity param=7.379774e-05
##   predicted class=B1  expected loss=0.7236181  P(node) =0.00144831
##     class counts:   110   101    63    99    25
##    probabilities: 0.276 0.254 0.158 0.249 0.063 
##   left son=32560 (179 obs) right son=32561 (219 obs)
##   Primary splits:
##       alzheimers        < 0.5    to the right, improve=2.797541, (0 missing)
##       ihd               < 0.5    to the right, improve=2.182276, (0 missing)
##       reimbursement2008 < 15500  to the right, improve=1.710577, (0 missing)
##       stroke            < 0.5    to the right, improve=1.223226, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=1.211249, (0 missing)
##   Surrogate splits:
##       stroke            < 0.5    to the right, agree=0.563, adj=0.028, (0 split)
##       reimbursement2008 < 15625  to the left,  agree=0.563, adj=0.028, (0 split)
##       age               < 62.5   to the left,  agree=0.555, adj=0.011, (0 split)
##       osteoporosis      < 0.5    to the right, agree=0.553, adj=0.006, (0 split)
## 
## Node number 16281: 59 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.6440678  P(node) =0.0002146993
##     class counts:    21     6    10    21     1
##    probabilities: 0.356 0.102 0.169 0.356 0.017 
##   left son=32562 (18 obs) right son=32563 (41 obs)
##   Primary splits:
##       reimbursement2008 < 19680  to the right, improve=1.9754260, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.9501021, (0 missing)
##       bucket2008        < 3.5    to the right, improve=0.8931654, (0 missing)
##       age               < 90.5   to the right, improve=0.7250257, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.5260164, (0 missing)
##   Surrogate splits:
##       bucket2008 < 3.5    to the right, agree=0.847, adj=0.5, (0 split)
## 
## Node number 27764: 343 observations,    complexity param=5.165842e-05
##   predicted class=B1  expected loss=0.5568513  P(node) =0.001248167
##     class counts:   152   136    37    16     2
##    probabilities: 0.443 0.397 0.108 0.047 0.006 
##   left son=55528 (117 obs) right son=55529 (226 obs)
##   Primary splits:
##       reimbursement2008 < 2835   to the right, improve=1.9282960, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.1581140, (0 missing)
##       age               < 82.5   to the right, improve=1.0933820, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.0145490, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.9380155, (0 missing)
## 
## Node number 27765: 129 observations
##   predicted class=B1  expected loss=0.4496124  P(node) =0.0004694272
##     class counts:    71    27    20     9     2
##    probabilities: 0.550 0.209 0.155 0.070 0.016 
## 
## Node number 27766: 218 observations,    complexity param=6.088314e-05
##   predicted class=B1  expected loss=0.5733945  P(node) =0.0007932956
##     class counts:    93    89    28     8     0
##    probabilities: 0.427 0.408 0.128 0.037 0.000 
##   left son=55532 (194 obs) right son=55533 (24 obs)
##   Primary splits:
##       reimbursement2008 < 2945   to the left,  improve=1.9617420, (0 missing)
##       depression        < 0.5    to the left,  improve=0.6526821, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.4610298, (0 missing)
##       age               < 57.5   to the left,  improve=0.4574831, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.3559027, (0 missing)
## 
## Node number 27767: 79 observations
##   predicted class=B2  expected loss=0.4810127  P(node) =0.0002874787
##     class counts:    26    41     7     5     0
##    probabilities: 0.329 0.519 0.089 0.063 0.000 
## 
## Node number 28002: 192 observations,    complexity param=7.19528e-05
##   predicted class=B2  expected loss=0.59375  P(node) =0.0006986823
##     class counts:    72    78    29    11     2
##    probabilities: 0.375 0.406 0.151 0.057 0.010 
##   left son=56004 (124 obs) right son=56005 (68 obs)
##   Primary splits:
##       age               < 78.5   to the left,  improve=3.1968100, (0 missing)
##       reimbursement2008 < 2885   to the left,  improve=2.1236740, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.0053880, (0 missing)
##       bucket2008        < 1.5    to the left,  improve=0.7479369, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.5316513, (0 missing)
## 
## Node number 28003: 74 observations,    complexity param=7.19528e-05
##   predicted class=B3  expected loss=0.6486486  P(node) =0.0002692838
##     class counts:    24    22    26     2     0
##    probabilities: 0.324 0.297 0.351 0.027 0.000 
##   left son=56006 (8 obs) right son=56007 (66 obs)
##   Primary splits:
##       cancer            < 0.5    to the right, improve=6.4864860, (0 missing)
##       age               < 65     to the left,  improve=1.7666590, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.7622440, (0 missing)
##       reimbursement2008 < 2355   to the right, improve=1.0927360, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.8745462, (0 missing)
## 
## Node number 28066: 169 observations
##   predicted class=B1  expected loss=0.5798817  P(node) =0.000614986
##     class counts:    71    58    32     6     2
##    probabilities: 0.420 0.343 0.189 0.036 0.012 
## 
## Node number 28067: 45 observations
##   predicted class=B2  expected loss=0.5111111  P(node) =0.0001637537
##     class counts:    12    22     9     2     0
##    probabilities: 0.267 0.489 0.200 0.044 0.000 
## 
## Node number 28354: 169 observations
##   predicted class=B1  expected loss=0.5621302  P(node) =0.000614986
##     class counts:    74    60    26     8     1
##    probabilities: 0.438 0.355 0.154 0.047 0.006 
## 
## Node number 28355: 62 observations
##   predicted class=B2  expected loss=0.4516129  P(node) =0.0002256162
##     class counts:    20    34     6     2     0
##    probabilities: 0.323 0.548 0.097 0.032 0.000 
## 
## Node number 28358: 689 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5718433  P(node) =0.002507251
##     class counts:   295   261    92    38     3
##    probabilities: 0.428 0.379 0.134 0.055 0.004 
##   left son=56716 (367 obs) right son=56717 (322 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the left,  improve=1.5366830, (0 missing)
##       reimbursement2008 < 2185   to the right, improve=0.9498001, (0 missing)
##       age               < 67.5   to the left,  improve=0.9450906, (0 missing)
##       copd              < 0.5    to the left,  improve=0.5052370, (0 missing)
##       depression        < 0.5    to the left,  improve=0.4336301, (0 missing)
##   Surrogate splits:
##       copd              < 0.5    to the left,  agree=0.605, adj=0.155, (0 split)
##       age               < 85.5   to the left,  agree=0.543, adj=0.022, (0 split)
##       reimbursement2008 < 2515   to the left,  agree=0.541, adj=0.019, (0 split)
##       alzheimers        < 0.5    to the left,  agree=0.538, adj=0.012, (0 split)
## 
## Node number 28359: 34 observations
##   predicted class=B2  expected loss=0.5294118  P(node) =0.000123725
##     class counts:     8    16     6     4     0
##    probabilities: 0.235 0.471 0.176 0.118 0.000 
## 
## Node number 30808: 253 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5454545  P(node) =0.0009206595
##     class counts:   115    89    30    17     2
##    probabilities: 0.455 0.352 0.119 0.067 0.008 
##   left son=61616 (245 obs) right son=61617 (8 obs)
##   Primary splits:
##       age               < 96     to the left,  improve=1.6668230, (0 missing)
##       reimbursement2008 < 8170   to the left,  improve=1.5801570, (0 missing)
##       depression        < 0.5    to the left,  improve=0.8012407, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.6406559, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.4810539, (0 missing)
## 
## Node number 30809: 56 observations
##   predicted class=B2  expected loss=0.5  P(node) =0.0002037823
##     class counts:    17    28     8     3     0
##    probabilities: 0.304 0.500 0.143 0.054 0.000 
## 
## Node number 30892: 1478 observations,    complexity param=9.962695e-05
##   predicted class=B2  expected loss=0.5886333  P(node) =0.005378398
##     class counts:   600   608   199    67     4
##    probabilities: 0.406 0.411 0.135 0.045 0.003 
##   left son=61784 (759 obs) right son=61785 (719 obs)
##   Primary splits:
##       reimbursement2008 < 4655   to the left,  improve=1.4912330, (0 missing)
##       age               < 59.5   to the right, improve=1.4379920, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.4252592, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.4189515, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.1287486, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.566, adj=0.108, (0 split)
##       alzheimers < 0.5    to the left,  agree=0.535, adj=0.043, (0 split)
##       stroke     < 0.5    to the left,  agree=0.533, adj=0.040, (0 split)
##       copd       < 0.5    to the left,  agree=0.530, adj=0.033, (0 split)
##       age        < 82.5   to the left,  agree=0.526, adj=0.025, (0 split)
## 
## Node number 30893: 49 observations
##   predicted class=B2  expected loss=0.4897959  P(node) =0.0001783096
##     class counts:    13    25     4     5     2
##    probabilities: 0.265 0.510 0.082 0.102 0.041 
## 
## Node number 30900: 298 observations
##   predicted class=B1  expected loss=0.5503356  P(node) =0.001084413
##     class counts:   134    94    46    20     4
##    probabilities: 0.450 0.315 0.154 0.067 0.013 
## 
## Node number 30901: 405 observations,    complexity param=8.855729e-05
##   predicted class=B2  expected loss=0.6296296  P(node) =0.001473783
##     class counts:   148   150    73    33     1
##    probabilities: 0.365 0.370 0.180 0.081 0.002 
##   left son=61802 (137 obs) right son=61803 (268 obs)
##   Primary splits:
##       reimbursement2008 < 5685   to the left,  improve=1.4352860, (0 missing)
##       age               < 43     to the right, improve=1.2563810, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=0.8108852, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.3644866, (0 missing)
##       copd              < 0.5    to the left,  improve=0.3421456, (0 missing)
## 
## Node number 30904: 274 observations,    complexity param=7.19528e-05
##   predicted class=B1  expected loss=0.5583942  P(node) =0.0009970779
##     class counts:   121    99    42    11     1
##    probabilities: 0.442 0.361 0.153 0.040 0.004 
##   left son=61808 (174 obs) right son=61809 (100 obs)
##   Primary splits:
##       alzheimers        < 0.5    to the left,  improve=2.2349370, (0 missing)
##       age               < 37.5   to the left,  improve=1.7714310, (0 missing)
##       reimbursement2008 < 4990   to the right, improve=1.7636660, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=0.8701440, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.4025273, (0 missing)
##   Surrogate splits:
##       stroke            < 0.5    to the left,  agree=0.668, adj=0.09, (0 split)
##       reimbursement2008 < 3085   to the right, agree=0.642, adj=0.02, (0 split)
## 
## Node number 30905: 23 observations
##   predicted class=B2  expected loss=0.3913043  P(node) =8.369632e-05
##     class counts:     5    14     3     1     0
##    probabilities: 0.217 0.609 0.130 0.043 0.000 
## 
## Node number 30918: 721 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5492372  P(node) =0.002623698
##     class counts:   234   325   114    44     4
##    probabilities: 0.325 0.451 0.158 0.061 0.006 
##   left son=61836 (109 obs) right son=61837 (612 obs)
##   Primary splits:
##       age               < 86.5   to the right, improve=5.2024390, (0 missing)
##       reimbursement2008 < 8105   to the left,  improve=1.9497410, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.3441110, (0 missing)
##       bucket2008        < 2.5    to the right, improve=0.8592657, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.1415473, (0 missing)
## 
## Node number 30919: 332 observations
##   predicted class=B2  expected loss=0.4939759  P(node) =0.001208138
##     class counts:    81   168    61    20     2
##    probabilities: 0.244 0.506 0.184 0.060 0.006 
## 
## Node number 30962: 368 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.625  P(node) =0.001339141
##     class counts:   138   132    66    32     0
##    probabilities: 0.375 0.359 0.179 0.087 0.000 
##   left son=61924 (261 obs) right son=61925 (107 obs)
##   Primary splits:
##       reimbursement2008 < 10440  to the right, improve=2.0386870, (0 missing)
##       age               < 68.5   to the right, improve=2.0238320, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=1.0604210, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.8507150, (0 missing)
##       heart.failure     < 0.5    to the right, improve=0.3195541, (0 missing)
## 
## Node number 30963: 188 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.6489362  P(node) =0.0006841264
##     class counts:    66    53    50    17     2
##    probabilities: 0.351 0.282 0.266 0.090 0.011 
##   left son=61926 (135 obs) right son=61927 (53 obs)
##   Primary splits:
##       age               < 55.5   to the right, improve=1.3142350, (0 missing)
##       reimbursement2008 < 8995   to the left,  improve=1.1323620, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.7672950, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.7658279, (0 missing)
##       bucket2008        < 3.5    to the right, improve=0.3998270, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 8645   to the right, agree=0.723, adj=0.019, (0 split)
## 
## Node number 31768: 44 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5227273  P(node) =0.0001601147
##     class counts:    21    13     5     5     0
##    probabilities: 0.477 0.295 0.114 0.114 0.000 
##   left son=63536 (26 obs) right son=63537 (18 obs)
##   Primary splits:
##       reimbursement2008 < 9180   to the left,  improve=3.34188000, (0 missing)
##       age               < 73.5   to the right, improve=1.53473700, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=1.08333300, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.99564270, (0 missing)
##       copd              < 0.5    to the right, improve=0.09090909, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.864, adj=0.667, (0 split)
##       age        < 82.5   to the left,  agree=0.636, adj=0.111, (0 split)
##       stroke     < 0.5    to the left,  agree=0.614, adj=0.056, (0 split)
## 
## Node number 31769: 49 observations
##   predicted class=B2  expected loss=0.4897959  P(node) =0.0001783096
##     class counts:    11    25    10     3     0
##    probabilities: 0.224 0.510 0.204 0.061 0.000 
## 
## Node number 31848: 50 observations
##   predicted class=B2  expected loss=0.56  P(node) =0.0001819485
##     class counts:    21    22     1     6     0
##    probabilities: 0.420 0.440 0.020 0.120 0.000 
## 
## Node number 31849: 260 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.6923077  P(node) =0.0009461323
##     class counts:    80    77    63    31     9
##    probabilities: 0.308 0.296 0.242 0.119 0.035 
##   left son=63698 (20 obs) right son=63699 (240 obs)
##   Primary splits:
##       reimbursement2008 < 14765  to the right, improve=1.866026, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.724179, (0 missing)
##       age               < 59     to the right, improve=1.389622, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.186623, (0 missing)
##       stroke            < 0.5    to the right, improve=0.396978, (0 missing)
## 
## Node number 31850: 56 observations
##   predicted class=B2  expected loss=0.5178571  P(node) =0.0002037823
##     class counts:    11    27     9     9     0
##    probabilities: 0.196 0.482 0.161 0.161 0.000 
## 
## Node number 31851: 181 observations,    complexity param=5.534831e-05
##   predicted class=B3  expected loss=0.6519337  P(node) =0.0006586537
##     class counts:    42    54    63    19     3
##    probabilities: 0.232 0.298 0.348 0.105 0.017 
##   left son=63702 (136 obs) right son=63703 (45 obs)
##   Primary splits:
##       reimbursement2008 < 6865   to the right, improve=2.6510090, (0 missing)
##       age               < 95.5   to the left,  improve=1.1712710, (0 missing)
##       bucket2008        < 2.5    to the right, improve=0.4758931, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.1841866, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.1010412, (0 missing)
## 
## Node number 32560: 179 observations,    complexity param=7.379774e-05
##   predicted class=B1  expected loss=0.6815642  P(node) =0.0006513757
##     class counts:    57    51    27    31    13
##    probabilities: 0.318 0.285 0.151 0.173 0.073 
##   left son=65120 (38 obs) right son=65121 (141 obs)
##   Primary splits:
##       reimbursement2008 < 21440  to the right, improve=2.8400160, (0 missing)
##       age               < 70.5   to the left,  improve=1.0471050, (0 missing)
##       stroke            < 0.5    to the right, improve=0.8887163, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.8119666, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.6975642, (0 missing)
## 
## Node number 32561: 219 observations
##   predicted class=B4  expected loss=0.6894977  P(node) =0.0007969345
##     class counts:    53    50    36    68    12
##    probabilities: 0.242 0.228 0.164 0.311 0.055 
## 
## Node number 32562: 18 observations
##   predicted class=B1  expected loss=0.4444444  P(node) =6.550147e-05
##     class counts:    10     2     0     5     1
##    probabilities: 0.556 0.111 0.000 0.278 0.056 
## 
## Node number 32563: 41 observations
##   predicted class=B4  expected loss=0.6097561  P(node) =0.0001491978
##     class counts:    11     4    10    16     0
##    probabilities: 0.268 0.098 0.244 0.390 0.000 
## 
## Node number 55528: 117 observations,    complexity param=5.165842e-05
##   predicted class=B1  expected loss=0.4871795  P(node) =0.0004257595
##     class counts:    60    38    15     3     1
##    probabilities: 0.513 0.325 0.128 0.026 0.009 
##   left son=111056 (78 obs) right son=111057 (39 obs)
##   Primary splits:
##       reimbursement2008 < 2945   to the left,  improve=2.9829060, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.2210830, (0 missing)
##       age               < 76.5   to the right, improve=1.1210830, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.9103070, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.2543679, (0 missing)
##   Surrogate splits:
##       age < 76.5   to the right, agree=0.684, adj=0.051, (0 split)
## 
## Node number 55529: 226 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5663717  P(node) =0.0008224073
##     class counts:    92    98    22    13     1
##    probabilities: 0.407 0.434 0.097 0.058 0.004 
##   left son=111058 (72 obs) right son=111059 (154 obs)
##   Primary splits:
##       age               < 80.5   to the right, improve=1.5914710, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.3555880, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.7668454, (0 missing)
##       reimbursement2008 < 2795   to the left,  improve=0.6874895, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.4980787, (0 missing)
## 
## Node number 55532: 194 observations
##   predicted class=B1  expected loss=0.556701  P(node) =0.0007059603
##     class counts:    86    74    27     7     0
##    probabilities: 0.443 0.381 0.139 0.036 0.000 
## 
## Node number 55533: 24 observations
##   predicted class=B2  expected loss=0.375  P(node) =8.733529e-05
##     class counts:     7    15     1     1     0
##    probabilities: 0.292 0.625 0.042 0.042 0.000 
## 
## Node number 56004: 124 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5403226  P(node) =0.0004512323
##     class counts:    57    47    16     4     0
##    probabilities: 0.460 0.379 0.129 0.032 0.000 
##   left son=112008 (46 obs) right son=112009 (78 obs)
##   Primary splits:
##       age               < 72.5   to the right, improve=2.8817380, (0 missing)
##       reimbursement2008 < 2885   to the left,  improve=1.5254660, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.4454760, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.7103829, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.4023915, (0 missing)
##   Surrogate splits:
##       stroke            < 0.5    to the right, agree=0.661, adj=0.087, (0 split)
##       reimbursement2008 < 2575   to the left,  agree=0.645, adj=0.043, (0 split)
## 
## Node number 56005: 68 observations
##   predicted class=B2  expected loss=0.5441176  P(node) =0.00024745
##     class counts:    15    31    13     7     2
##    probabilities: 0.221 0.456 0.191 0.103 0.029 
## 
## Node number 56006: 8 observations
##   predicted class=B2  expected loss=0  P(node) =2.911176e-05
##     class counts:     0     8     0     0     0
##    probabilities: 0.000 1.000 0.000 0.000 0.000 
## 
## Node number 56007: 66 observations,    complexity param=5.534831e-05
##   predicted class=B3  expected loss=0.6060606  P(node) =0.0002401721
##     class counts:    24    14    26     2     0
##    probabilities: 0.364 0.212 0.394 0.030 0.000 
##   left son=112014 (40 obs) right son=112015 (26 obs)
##   Primary splits:
##       alzheimers        < 0.5    to the left,  improve=2.3576920, (0 missing)
##       age               < 65     to the left,  improve=1.8352940, (0 missing)
##       reimbursement2008 < 2375   to the right, improve=1.1494920, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=0.1363636, (0 missing)
##   Surrogate splits:
##       age < 82.5   to the left,  agree=0.652, adj=0.115, (0 split)
## 
## Node number 56716: 367 observations
##   predicted class=B1  expected loss=0.5395095  P(node) =0.001335502
##     class counts:   169   131    51    13     3
##    probabilities: 0.460 0.357 0.139 0.035 0.008 
## 
## Node number 56717: 322 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.5962733  P(node) =0.001171748
##     class counts:   126   130    41    25     0
##    probabilities: 0.391 0.404 0.127 0.078 0.000 
##   left son=113434 (78 obs) right son=113435 (244 obs)
##   Primary splits:
##       age               < 67.5   to the left,  improve=2.0124890, (0 missing)
##       reimbursement2008 < 2265   to the right, improve=1.1949400, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.3273471, (0 missing)
##       depression        < 0.5    to the right, improve=0.1786959, (0 missing)
##       copd              < 0.5    to the left,  improve=0.1745923, (0 missing)
## 
## Node number 61616: 245 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5346939  P(node) =0.0008915478
##     class counts:   114    87    27    16     1
##    probabilities: 0.465 0.355 0.110 0.065 0.004 
##   left son=123232 (209 obs) right son=123233 (36 obs)
##   Primary splits:
##       reimbursement2008 < 8170   to the left,  improve=1.7182870, (0 missing)
##       age               < 90.5   to the right, improve=1.6062760, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.7459219, (0 missing)
##       depression        < 0.5    to the left,  improve=0.6596720, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.6366849, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.971, adj=0.806, (0 split)
## 
## Node number 61617: 8 observations
##   predicted class=B3  expected loss=0.625  P(node) =2.911176e-05
##     class counts:     1     2     3     1     1
##    probabilities: 0.125 0.250 0.375 0.125 0.125 
## 
## Node number 61784: 759 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5678524  P(node) =0.002761979
##     class counts:   328   303    94    33     1
##    probabilities: 0.432 0.399 0.124 0.043 0.001 
##   left son=123568 (158 obs) right son=123569 (601 obs)
##   Primary splits:
##       reimbursement2008 < 4315   to the right, improve=1.62186500, (0 missing)
##       age               < 82.5   to the right, improve=0.60286370, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.24697950, (0 missing)
##       copd              < 0.5    to the left,  improve=0.10233690, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.09394217, (0 missing)
## 
## Node number 61785: 719 observations,    complexity param=9.962695e-05
##   predicted class=B2  expected loss=0.5757997  P(node) =0.00261642
##     class counts:   272   305   105    34     3
##    probabilities: 0.378 0.424 0.146 0.047 0.004 
##   left son=123570 (346 obs) right son=123571 (373 obs)
##   Primary splits:
##       reimbursement2008 < 5835   to the left,  improve=2.8015510, (0 missing)
##       age               < 59.5   to the right, improve=2.2849680, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.5855315, (0 missing)
##       stroke            < 0.5    to the right, improve=0.5109046, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.2469968, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.590, adj=0.147, (0 split)
##       alzheimers < 0.5    to the left,  agree=0.537, adj=0.038, (0 split)
##       age        < 60.5   to the left,  agree=0.527, adj=0.017, (0 split)
## 
## Node number 61802: 137 observations
##   predicted class=B1  expected loss=0.5839416  P(node) =0.000498539
##     class counts:    57    43    22    15     0
##    probabilities: 0.416 0.314 0.161 0.109 0.000 
## 
## Node number 61803: 268 observations
##   predicted class=B2  expected loss=0.6007463  P(node) =0.0009752441
##     class counts:    91   107    51    18     1
##    probabilities: 0.340 0.399 0.190 0.067 0.004 
## 
## Node number 61808: 174 observations
##   predicted class=B1  expected loss=0.5229885  P(node) =0.0006331809
##     class counts:    83    53    29     8     1
##    probabilities: 0.477 0.305 0.167 0.046 0.006 
## 
## Node number 61809: 100 observations,    complexity param=7.19528e-05
##   predicted class=B2  expected loss=0.54  P(node) =0.000363897
##     class counts:    38    46    13     3     0
##    probabilities: 0.380 0.460 0.130 0.030 0.000 
##   left son=123618 (26 obs) right son=123619 (74 obs)
##   Primary splits:
##       reimbursement2008 < 4355   to the right, improve=5.3372560, (0 missing)
##       age               < 62.5   to the right, improve=1.9704690, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.2703500, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.5886275, (0 missing)
##   Surrogate splits:
##       age < 50.5   to the left,  agree=0.79, adj=0.192, (0 split)
## 
## Node number 61836: 109 observations
##   predicted class=B1  expected loss=0.5412844  P(node) =0.0003966478
##     class counts:    50    33    16     9     1
##    probabilities: 0.459 0.303 0.147 0.083 0.009 
## 
## Node number 61837: 612 observations
##   predicted class=B2  expected loss=0.5228758  P(node) =0.00222705
##     class counts:   184   292    98    35     3
##    probabilities: 0.301 0.477 0.160 0.057 0.005 
## 
## Node number 61924: 261 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5862069  P(node) =0.0009497713
##     class counts:   108    87    44    22     0
##    probabilities: 0.414 0.333 0.169 0.084 0.000 
##   left son=123848 (92 obs) right son=123849 (169 obs)
##   Primary splits:
##       reimbursement2008 < 12585  to the left,  improve=2.1315740, (0 missing)
##       age               < 77.5   to the right, improve=1.2761660, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.0543160, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=1.0296720, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.5202291, (0 missing)
## 
## Node number 61925: 107 observations
##   predicted class=B2  expected loss=0.5794393  P(node) =0.0003893698
##     class counts:    30    45    22    10     0
##    probabilities: 0.280 0.421 0.206 0.093 0.000 
## 
## Node number 61926: 135 observations
##   predicted class=B1  expected loss=0.6074074  P(node) =0.000491261
##     class counts:    53    34    36    12     0
##    probabilities: 0.393 0.252 0.267 0.089 0.000 
## 
## Node number 61927: 53 observations
##   predicted class=B2  expected loss=0.6415094  P(node) =0.0001928654
##     class counts:    13    19    14     5     2
##    probabilities: 0.245 0.358 0.264 0.094 0.038 
## 
## Node number 63536: 26 observations
##   predicted class=B1  expected loss=0.3461538  P(node) =9.461323e-05
##     class counts:    17     4     2     3     0
##    probabilities: 0.654 0.154 0.077 0.115 0.000 
## 
## Node number 63537: 18 observations
##   predicted class=B2  expected loss=0.5  P(node) =6.550147e-05
##     class counts:     4     9     3     2     0
##    probabilities: 0.222 0.500 0.167 0.111 0.000 
## 
## Node number 63698: 20 observations
##   predicted class=B1  expected loss=0.45  P(node) =7.277941e-05
##     class counts:    11     5     2     1     1
##    probabilities: 0.550 0.250 0.100 0.050 0.050 
## 
## Node number 63699: 240 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.7  P(node) =0.0008733529
##     class counts:    69    72    61    30     8
##    probabilities: 0.288 0.300 0.254 0.125 0.033 
##   left son=127398 (201 obs) right son=127399 (39 obs)
##   Primary splits:
##       age               < 61.5   to the right, improve=1.4580460, (0 missing)
##       reimbursement2008 < 10970  to the right, improve=1.4206140, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.0755290, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.9752886, (0 missing)
##       stroke            < 0.5    to the right, improve=0.4524283, (0 missing)
## 
## Node number 63702: 136 observations
##   predicted class=B3  expected loss=0.6029412  P(node) =0.0004949
##     class counts:    34    36    54    10     2
##    probabilities: 0.250 0.265 0.397 0.074 0.015 
## 
## Node number 63703: 45 observations
##   predicted class=B2  expected loss=0.6  P(node) =0.0001637537
##     class counts:     8    18     9     9     1
##    probabilities: 0.178 0.400 0.200 0.200 0.022 
## 
## Node number 65120: 38 observations
##   predicted class=B2  expected loss=0.5  P(node) =0.0001382809
##     class counts:     9    19     4     5     1
##    probabilities: 0.237 0.500 0.105 0.132 0.026 
## 
## Node number 65121: 141 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.6595745  P(node) =0.0005130948
##     class counts:    48    32    23    26    12
##    probabilities: 0.340 0.227 0.163 0.184 0.085 
##   left son=130242 (89 obs) right son=130243 (52 obs)
##   Primary splits:
##       reimbursement2008 < 17585  to the right, improve=2.1889060, (0 missing)
##       age               < 47.5   to the right, improve=1.2186760, (0 missing)
##       bucket2008        < 3.5    to the right, improve=1.1702130, (0 missing)
##       stroke            < 0.5    to the right, improve=0.9175166, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.5919705, (0 missing)
##   Surrogate splits:
##       bucket2008 < 3.5    to the right, agree=0.702, adj=0.192, (0 split)
##       age        < 47.5   to the right, agree=0.667, adj=0.096, (0 split)
## 
## Node number 111056: 78 observations
##   predicted class=B1  expected loss=0.4102564  P(node) =0.0002838397
##     class counts:    46    19    11     2     0
##    probabilities: 0.590 0.244 0.141 0.026 0.000 
## 
## Node number 111057: 39 observations
##   predicted class=B2  expected loss=0.5128205  P(node) =0.0001419198
##     class counts:    14    19     4     1     1
##    probabilities: 0.359 0.487 0.103 0.026 0.026 
## 
## Node number 111058: 72 observations
##   predicted class=B1  expected loss=0.4861111  P(node) =0.0002620059
##     class counts:    37    29     5     1     0
##    probabilities: 0.514 0.403 0.069 0.014 0.000 
## 
## Node number 111059: 154 observations
##   predicted class=B2  expected loss=0.5519481  P(node) =0.0005604015
##     class counts:    55    69    17    12     1
##    probabilities: 0.357 0.448 0.110 0.078 0.006 
## 
## Node number 112008: 46 observations
##   predicted class=B1  expected loss=0.4347826  P(node) =0.0001673926
##     class counts:    26    10     8     2     0
##    probabilities: 0.565 0.217 0.174 0.043 0.000 
## 
## Node number 112009: 78 observations
##   predicted class=B2  expected loss=0.525641  P(node) =0.0002838397
##     class counts:    31    37     8     2     0
##    probabilities: 0.397 0.474 0.103 0.026 0.000 
## 
## Node number 112014: 40 observations
##   predicted class=B1  expected loss=0.6  P(node) =0.0001455588
##     class counts:    16    12    11     1     0
##    probabilities: 0.400 0.300 0.275 0.025 0.000 
## 
## Node number 112015: 26 observations
##   predicted class=B3  expected loss=0.4230769  P(node) =9.461323e-05
##     class counts:     8     2    15     1     0
##    probabilities: 0.308 0.077 0.577 0.038 0.000 
## 
## Node number 113434: 78 observations
##   predicted class=B1  expected loss=0.5512821  P(node) =0.0002838397
##     class counts:    35    23    15     5     0
##    probabilities: 0.449 0.295 0.192 0.064 0.000 
## 
## Node number 113435: 244 observations
##   predicted class=B2  expected loss=0.5614754  P(node) =0.0008879088
##     class counts:    91   107    26    20     0
##    probabilities: 0.373 0.439 0.107 0.082 0.000 
## 
## Node number 123232: 209 observations
##   predicted class=B1  expected loss=0.507177  P(node) =0.0007605448
##     class counts:   103    70    22    14     0
##    probabilities: 0.493 0.335 0.105 0.067 0.000 
## 
## Node number 123233: 36 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.5277778  P(node) =0.0001310029
##     class counts:    11    17     5     2     1
##    probabilities: 0.306 0.472 0.139 0.056 0.028 
##   left son=246466 (15 obs) right son=246467 (21 obs)
##   Primary splits:
##       age               < 74.5   to the left,  improve=5.3968250, (0 missing)
##       reimbursement2008 < 8705   to the right, improve=1.5053320, (0 missing)
##       copd              < 0.5    to the right, improve=0.3703704, (0 missing)
##       depression        < 0.5    to the right, improve=0.3527778, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.2972583, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 8460   to the left,  agree=0.611, adj=0.067, (0 split)
## 
## Node number 123568: 158 observations
##   predicted class=B1  expected loss=0.5  P(node) =0.0005749573
##     class counts:    79    55    15     8     1
##    probabilities: 0.500 0.348 0.095 0.051 0.006 
## 
## Node number 123569: 601 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5856905  P(node) =0.002187021
##     class counts:   249   248    79    25     0
##    probabilities: 0.414 0.413 0.131 0.042 0.000 
##   left son=247138 (592 obs) right son=247139 (9 obs)
##   Primary splits:
##       reimbursement2008 < 4295   to the left,  improve=3.0859230, (0 missing)
##       age               < 62.5   to the right, improve=0.8258296, (0 missing)
##       copd              < 0.5    to the left,  improve=0.2730143, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.1209200, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.1102521, (0 missing)
## 
## Node number 123570: 346 observations
##   predicted class=B2  expected loss=0.5202312  P(node) =0.001259084
##     class counts:   122   166    44    13     1
##    probabilities: 0.353 0.480 0.127 0.038 0.003 
## 
## Node number 123571: 373 observations,    complexity param=9.962695e-05
##   predicted class=B1  expected loss=0.5978552  P(node) =0.001357336
##     class counts:   150   139    61    21     2
##    probabilities: 0.402 0.373 0.164 0.056 0.005 
##   left son=247142 (124 obs) right son=247143 (249 obs)
##   Primary splits:
##       alzheimers        < 0.5    to the right, improve=1.9370400, (0 missing)
##       reimbursement2008 < 6045   to the right, improve=1.9317030, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=1.1351660, (0 missing)
##       age               < 68.5   to the left,  improve=0.9923350, (0 missing)
##       stroke            < 0.5    to the right, improve=0.8206414, (0 missing)
##   Surrogate splits:
##       age               < 64.5   to the left,  agree=0.673, adj=0.016, (0 split)
##       stroke            < 0.5    to the right, agree=0.673, adj=0.016, (0 split)
##       reimbursement2008 < 5845   to the left,  agree=0.670, adj=0.008, (0 split)
## 
## Node number 123618: 26 observations
##   predicted class=B1  expected loss=0.3846154  P(node) =9.461323e-05
##     class counts:    16     4     4     2     0
##    probabilities: 0.615 0.154 0.154 0.077 0.000 
## 
## Node number 123619: 74 observations
##   predicted class=B2  expected loss=0.4324324  P(node) =0.0002692838
##     class counts:    22    42     9     1     0
##    probabilities: 0.297 0.568 0.122 0.014 0.000 
## 
## Node number 123848: 92 observations
##   predicted class=B1  expected loss=0.5  P(node) =0.0003347853
##     class counts:    46    23    17     6     0
##    probabilities: 0.500 0.250 0.185 0.065 0.000 
## 
## Node number 123849: 169 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.6213018  P(node) =0.000614986
##     class counts:    62    64    27    16     0
##    probabilities: 0.367 0.379 0.160 0.095 0.000 
##   left son=247698 (109 obs) right son=247699 (60 obs)
##   Primary splits:
##       reimbursement2008 < 14485  to the right, improve=2.3703890, (0 missing)
##       age               < 77.5   to the right, improve=1.8205180, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.5605270, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.9473954, (0 missing)
##       stroke            < 0.5    to the right, improve=0.8779250, (0 missing)
## 
## Node number 127398: 201 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.6865672  P(node) =0.0007314331
##     class counts:    63    58    49    27     4
##    probabilities: 0.313 0.289 0.244 0.134 0.020 
##   left son=254796 (112 obs) right son=254797 (89 obs)
##   Primary splits:
##       reimbursement2008 < 12625  to the left,  improve=2.6465710, (0 missing)
##       age               < 72.5   to the right, improve=1.5701210, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.5204340, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.8281641, (0 missing)
##       stroke            < 0.5    to the right, improve=0.4454147, (0 missing)
##   Surrogate splits:
##       age < 67.5   to the right, agree=0.587, adj=0.067, (0 split)
## 
## Node number 127399: 39 observations
##   predicted class=B2  expected loss=0.6410256  P(node) =0.0001419198
##     class counts:     6    14    12     3     4
##    probabilities: 0.154 0.359 0.308 0.077 0.103 
## 
## Node number 130242: 89 observations
##   predicted class=B1  expected loss=0.5955056  P(node) =0.0003238684
##     class counts:    36    15    17    14     7
##    probabilities: 0.404 0.169 0.191 0.157 0.079 
## 
## Node number 130243: 52 observations
##   predicted class=B2  expected loss=0.6730769  P(node) =0.0001892265
##     class counts:    12    17     6    12     5
##    probabilities: 0.231 0.327 0.115 0.231 0.096 
## 
## Node number 246466: 15 observations
##   predicted class=B1  expected loss=0.4  P(node) =5.458456e-05
##     class counts:     9     2     3     0     1
##    probabilities: 0.600 0.133 0.200 0.000 0.067 
## 
## Node number 246467: 21 observations
##   predicted class=B2  expected loss=0.2857143  P(node) =7.641838e-05
##     class counts:     2    15     2     2     0
##    probabilities: 0.095 0.714 0.095 0.095 0.000 
## 
## Node number 247138: 592 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5810811  P(node) =0.002154271
##     class counts:   248   240    79    25     0
##    probabilities: 0.419 0.405 0.133 0.042 0.000 
##   left son=494276 (135 obs) right son=494277 (457 obs)
##   Primary splits:
##       age               < 82.5   to the right, improve=1.0162580, (0 missing)
##       reimbursement2008 < 3485   to the left,  improve=0.9533819, (0 missing)
##       copd              < 0.5    to the left,  improve=0.2603666, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.1489946, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.1384892, (0 missing)
## 
## Node number 247139: 9 observations
##   predicted class=B2  expected loss=0.1111111  P(node) =3.275073e-05
##     class counts:     1     8     0     0     0
##    probabilities: 0.111 0.889 0.000 0.000 0.000 
## 
## Node number 247142: 124 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.516129  P(node) =0.0004512323
##     class counts:    60    39    19     5     1
##    probabilities: 0.484 0.315 0.153 0.040 0.008 
##   left son=494284 (114 obs) right son=494285 (10 obs)
##   Primary splits:
##       reimbursement2008 < 8555   to the left,  improve=3.2894170, (0 missing)
##       age               < 62.5   to the right, improve=1.3134040, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=0.8306452, (0 missing)
##       stroke            < 0.5    to the right, improve=0.6624062, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.6169185, (0 missing)
## 
## Node number 247143: 249 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.5983936  P(node) =0.0009061036
##     class counts:    90   100    42    16     1
##    probabilities: 0.361 0.402 0.169 0.064 0.004 
##   left son=494286 (217 obs) right son=494287 (32 obs)
##   Primary splits:
##       reimbursement2008 < 6045   to the right, improve=2.8382200, (0 missing)
##       age               < 68.5   to the left,  improve=1.5757780, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.8580882, (0 missing)
##       copd              < 0.5    to the left,  improve=0.4427711, (0 missing)
##       stroke            < 0.5    to the right, improve=0.2244234, (0 missing)
## 
## Node number 247698: 109 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5779817  P(node) =0.0003966478
##     class counts:    46    34    19    10     0
##    probabilities: 0.422 0.312 0.174 0.092 0.000 
##   left son=495396 (72 obs) right son=495397 (37 obs)
##   Primary splits:
##       osteoporosis      < 0.5    to the left,  improve=2.1824740, (0 missing)
##       reimbursement2008 < 17235  to the right, improve=1.3957040, (0 missing)
##       age               < 70.5   to the left,  improve=1.2827700, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.2406940, (0 missing)
##       bucket2008        < 3.5    to the left,  improve=0.2455781, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 14660  to the right, agree=0.679, adj=0.054, (0 split)
## 
## Node number 247699: 60 observations
##   predicted class=B2  expected loss=0.5  P(node) =0.0002183382
##     class counts:    16    30     8     6     0
##    probabilities: 0.267 0.500 0.133 0.100 0.000 
## 
## Node number 254796: 112 observations
##   predicted class=B1  expected loss=0.6160714  P(node) =0.0004075647
##     class counts:    43    27    31    10     1
##    probabilities: 0.384 0.241 0.277 0.089 0.009 
## 
## Node number 254797: 89 observations
##   predicted class=B2  expected loss=0.6516854  P(node) =0.0003238684
##     class counts:    20    31    18    17     3
##    probabilities: 0.225 0.348 0.202 0.191 0.034 
## 
## Node number 494276: 135 observations
##   predicted class=B1  expected loss=0.5259259  P(node) =0.000491261
##     class counts:    64    49    20     2     0
##    probabilities: 0.474 0.363 0.148 0.015 0.000 
## 
## Node number 494277: 457 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.5820569  P(node) =0.00166301
##     class counts:   184   191    59    23     0
##    probabilities: 0.403 0.418 0.129 0.050 0.000 
##   left son=988554 (290 obs) right son=988555 (167 obs)
##   Primary splits:
##       age               < 74.5   to the left,  improve=0.9874503, (0 missing)
##       reimbursement2008 < 3495   to the left,  improve=0.9861916, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.3272674, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.2337493, (0 missing)
##       copd              < 0.5    to the left,  improve=0.1994562, (0 missing)
##   Surrogate splits:
##       alzheimers < 0.5    to the left,  agree=0.637, adj=0.006, (0 split)
## 
## Node number 494284: 114 observations
##   predicted class=B1  expected loss=0.4824561  P(node) =0.0004148426
##     class counts:    59    32    18     4     1
##    probabilities: 0.518 0.281 0.158 0.035 0.009 
## 
## Node number 494285: 10 observations
##   predicted class=B2  expected loss=0.3  P(node) =3.63897e-05
##     class counts:     1     7     1     1     0
##    probabilities: 0.100 0.700 0.100 0.100 0.000 
## 
## Node number 494286: 217 observations
##   predicted class=B2  expected loss=0.5714286  P(node) =0.0007896566
##     class counts:    78    93    30    15     1
##    probabilities: 0.359 0.429 0.138 0.069 0.005 
## 
## Node number 494287: 32 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.625  P(node) =0.0001164471
##     class counts:    12     7    12     1     0
##    probabilities: 0.375 0.219 0.375 0.031 0.000 
##   left son=988574 (11 obs) right son=988575 (21 obs)
##   Primary splits:
##       age               < 72.5   to the left,  improve=1.8097940, (0 missing)
##       reimbursement2008 < 5975   to the left,  improve=0.7232143, (0 missing)
##       copd              < 0.5    to the left,  improve=0.6875000, (0 missing)
## 
## Node number 495396: 72 observations
##   predicted class=B1  expected loss=0.5138889  P(node) =0.0002620059
##     class counts:    35    17    12     8     0
##    probabilities: 0.486 0.236 0.167 0.111 0.000 
## 
## Node number 495397: 37 observations
##   predicted class=B2  expected loss=0.5405405  P(node) =0.0001346419
##     class counts:    11    17     7     2     0
##    probabilities: 0.297 0.459 0.189 0.054 0.000 
## 
## Node number 988554: 290 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5724138  P(node) =0.001055301
##     class counts:   124   114    37    15     0
##    probabilities: 0.428 0.393 0.128 0.052 0.000 
##   left son=1977108 (234 obs) right son=1977109 (56 obs)
##   Primary splits:
##       age               < 62.5   to the right, improve=1.0825800, (0 missing)
##       reimbursement2008 < 3945   to the right, improve=0.7040408, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.6026089, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.2655768, (0 missing)
##       copd              < 0.5    to the left,  improve=0.1804923, (0 missing)
## 
## Node number 988555: 167 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.5389222  P(node) =0.0006077081
##     class counts:    60    77    22     8     0
##    probabilities: 0.359 0.461 0.132 0.048 0.000 
##   left son=1977110 (39 obs) right son=1977111 (128 obs)
##   Primary splits:
##       reimbursement2008 < 4105   to the right, improve=1.3886510, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.7439669, (0 missing)
##       age               < 81.5   to the left,  improve=0.4824922, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.2060442, (0 missing)
##       copd              < 0.5    to the left,  improve=0.1297289, (0 missing)
## 
## Node number 988574: 11 observations
##   predicted class=B1  expected loss=0.3636364  P(node) =4.002868e-05
##     class counts:     7     2     2     0     0
##    probabilities: 0.636 0.182 0.182 0.000 0.000 
## 
## Node number 988575: 21 observations
##   predicted class=B3  expected loss=0.5238095  P(node) =7.641838e-05
##     class counts:     5     5    10     1     0
##    probabilities: 0.238 0.238 0.476 0.048 0.000 
## 
## Node number 1977108: 234 observations
##   predicted class=B1  expected loss=0.5470085  P(node) =0.0008515191
##     class counts:   106    89    28    11     0
##    probabilities: 0.453 0.380 0.120 0.047 0.000 
## 
## Node number 1977109: 56 observations
##   predicted class=B2  expected loss=0.5535714  P(node) =0.0002037823
##     class counts:    18    25     9     4     0
##    probabilities: 0.321 0.446 0.161 0.071 0.000 
## 
## Node number 1977110: 39 observations
##   predicted class=B1  expected loss=0.5128205  P(node) =0.0001419198
##     class counts:    19    14     5     1     0
##    probabilities: 0.487 0.359 0.128 0.026 0.000 
## 
## Node number 1977111: 128 observations
##   predicted class=B2  expected loss=0.5078125  P(node) =0.0004657882
##     class counts:    41    63    17     7     0
##    probabilities: 0.320 0.492 0.133 0.055 0.000 
## 
## n= 274803 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
##       1) root 274803 90337 B1 (0.67 0.19 0.089 0.043 0.0058)  
##         2) reimbursement2008< 1565 165987 20938 B1 (0.87 0.074 0.037 0.014 0.0014) *
##         3) reimbursement2008>=1565 108816 68841 B2 (0.36 0.37 0.17 0.088 0.012)  
##           6) reimbursement2008< 3065 39298 18853 B1 (0.52 0.31 0.12 0.045 0.0046)  
##            12) reimbursement2008< 2175 20077  8527 B1 (0.58 0.27 0.11 0.042 0.0038)  
##              24) diabetes< 0.5 8826  3280 B1 (0.63 0.24 0.091 0.035 0.0029) *
##              25) diabetes>=0.5 11251  5247 B1 (0.53 0.29 0.12 0.046 0.0045)  
##                50) kidney< 0.5 9007  4045 B1 (0.55 0.28 0.12 0.042 0.0044)  
##                 100) reimbursement2008< 1875 4935  2071 B1 (0.58 0.26 0.11 0.042 0.0045) *
##                 101) reimbursement2008>=1875 4072  1974 B1 (0.52 0.31 0.13 0.042 0.0044)  
##                   202) cancer< 0.5 3786  1807 B1 (0.52 0.3 0.13 0.041 0.0048) *
##                   203) cancer>=0.5 286   167 B1 (0.42 0.4 0.13 0.056 0)  
##                     406) age< 73.5 128    64 B1 (0.5 0.33 0.11 0.062 0)  
##                       812) depression< 0.5 95    41 B1 (0.57 0.26 0.12 0.053 0) *
##                       813) depression>=0.5 33    16 B2 (0.3 0.52 0.091 0.091 0) *
##                     407) age>=73.5 158    85 B2 (0.35 0.46 0.14 0.051 0) *
##                51) kidney>=0.5 2244  1202 B1 (0.46 0.33 0.14 0.064 0.0049)  
##                 102) heart.failure< 0.5 992   477 B1 (0.52 0.29 0.13 0.057 0.002) *
##                 103) heart.failure>=0.5 1252   725 B1 (0.42 0.36 0.15 0.069 0.0072)  
##                   206) arthritis< 0.5 904   486 B1 (0.46 0.34 0.13 0.063 0.0066)  
##                     412) reimbursement2008< 1735 270   125 B1 (0.54 0.27 0.13 0.056 0.0037) *
##                     413) reimbursement2008>=1735 634   361 B1 (0.43 0.36 0.13 0.066 0.0079)  
##                       826) age< 91.5 596   330 B1 (0.45 0.36 0.13 0.057 0.0084)  
##                        1652) reimbursement2008>=1765 555   302 B1 (0.46 0.35 0.14 0.052 0.0072)  
##                          3304) reimbursement2008< 1955 265   130 B1 (0.51 0.31 0.13 0.049 0.0038)  
##                            6608) stroke< 0.5 251   118 B1 (0.53 0.29 0.13 0.044 0.004)  
##                             13216) cancer< 0.5 235   106 B1 (0.55 0.27 0.13 0.047 0.0043) *
##                             13217) cancer>=0.5 16     7 B2 (0.25 0.56 0.19 0 0) *
##                            6609) stroke>=0.5 14     5 B2 (0.14 0.64 0.071 0.14 0) *
##                          3305) reimbursement2008>=1955 290   172 B1 (0.41 0.38 0.14 0.055 0.01)  
##                            6610) age< 81.5 213   120 B1 (0.44 0.35 0.14 0.07 0.0047)  
##                             13220) age>=44.5 201   110 B1 (0.45 0.33 0.14 0.07 0) *
##                             13221) age< 44.5 12     5 B2 (0.17 0.58 0.083 0.083 0.083) *
##                            6611) age>=81.5 77    40 B2 (0.32 0.48 0.16 0.013 0.026) *
##                        1653) reimbursement2008< 1765 41    20 B2 (0.32 0.51 0.024 0.12 0.024) *
##                       827) age>=91.5 38    21 B2 (0.18 0.45 0.16 0.21 0) *
##                   207) arthritis>=0.5 348   205 B2 (0.31 0.41 0.18 0.086 0.0086) *
##            13) reimbursement2008>=2175 19221 10326 B1 (0.46 0.35 0.13 0.049 0.0054)  
##              26) diabetes< 0.5 7137  3360 B1 (0.53 0.31 0.11 0.042 0.0046)  
##                52) arthritis< 0.5 5554  2471 B1 (0.56 0.3 0.1 0.039 0.0049)  
##                 104) ihd< 0.5 2348   933 B1 (0.6 0.27 0.092 0.031 0.0051) *
##                 105) ihd>=0.5 3206  1538 B1 (0.52 0.32 0.11 0.045 0.0047)  
##                   210) depression< 0.5 2325  1056 B1 (0.55 0.3 0.11 0.043 0.0052) *
##                   211) depression>=0.5 881   482 B1 (0.45 0.36 0.13 0.052 0.0034)  
##                     422) kidney< 0.5 763   405 B1 (0.47 0.34 0.13 0.052 0.0039) *
##                     423) kidney>=0.5 118    63 B2 (0.35 0.47 0.14 0.051 0)  
##                       846) reimbursement2008>=2865 22    10 B1 (0.55 0.23 0.18 0.045 0) *
##                       847) reimbursement2008< 2865 96    46 B2 (0.3 0.52 0.12 0.052 0) *
##                53) arthritis>=0.5 1583   889 B1 (0.44 0.37 0.14 0.052 0.0038)  
##                 106) stroke< 0.5 1525   844 B1 (0.45 0.36 0.13 0.054 0.0039)  
##                   212) cancer< 0.5 1438   784 B1 (0.45 0.36 0.13 0.053 0.0042)  
##                     424) reimbursement2008>=2715 495   280 B1 (0.43 0.41 0.1 0.053 0.004)  
##                       848) reimbursement2008>=2795 385   210 B1 (0.45 0.38 0.1 0.06 0.0052)  
##                        1696) age< 80.5 263   131 B1 (0.5 0.36 0.099 0.042 0) *
##                        1697) age>=80.5 122    70 B2 (0.35 0.43 0.11 0.098 0.016) *
##                       849) reimbursement2008< 2795 110    54 B2 (0.36 0.51 0.1 0.027 0) *
##                     425) reimbursement2008< 2715 943   504 B1 (0.47 0.33 0.15 0.053 0.0042) *
##                   213) cancer>=0.5 87    48 B2 (0.31 0.45 0.17 0.069 0) *
##                 107) stroke>=0.5 58    26 B2 (0.22 0.55 0.21 0.017 0) *
##              27) diabetes>=0.5 12084  6966 B1 (0.42 0.37 0.15 0.054 0.0059)  
##                54) arthritis< 0.5 8413  4653 B1 (0.45 0.35 0.15 0.052 0.0056)  
##                 108) heart.failure< 0.5 4375  2220 B1 (0.49 0.34 0.13 0.039 0.0039)  
##                   216) cancer< 0.5 3992  1978 B1 (0.5 0.33 0.12 0.038 0.0033)  
##                     432) ihd< 0.5 1265   562 B1 (0.56 0.29 0.12 0.035 0.0032) *
##                     433) ihd>=0.5 2727  1416 B1 (0.48 0.35 0.13 0.04 0.0033)  
##                       866) reimbursement2008< 2615 1499   736 B1 (0.51 0.33 0.13 0.035 0.002) *
##                       867) reimbursement2008>=2615 1228   680 B1 (0.45 0.37 0.13 0.046 0.0049)  
##                        1734) reimbursement2008>=2995 171    81 B1 (0.53 0.27 0.14 0.058 0.0058) *
##                        1735) reimbursement2008< 2995 1057   599 B1 (0.43 0.39 0.13 0.044 0.0047)  
##                          3470) age< 83.5 840   458 B1 (0.45 0.37 0.12 0.049 0.006)  
##                            6940) age< 54.5 71    31 B1 (0.56 0.23 0.15 0.042 0.014) *
##                            6941) age>=54.5 769   427 B1 (0.44 0.38 0.12 0.049 0.0052)  
##                             13882) age>=70.5 472   249 B1 (0.47 0.35 0.12 0.053 0.0085)  
##                               27764) age>=73.5 343   191 B1 (0.44 0.4 0.11 0.047 0.0058)  
##                                 55528) reimbursement2008>=2835 117    57 B1 (0.51 0.32 0.13 0.026 0.0085)  
##                                  111056) reimbursement2008< 2945 78    32 B1 (0.59 0.24 0.14 0.026 0) *
##                                  111057) reimbursement2008>=2945 39    20 B2 (0.36 0.49 0.1 0.026 0.026) *
##                                 55529) reimbursement2008< 2835 226   128 B2 (0.41 0.43 0.097 0.058 0.0044)  
##                                  111058) age>=80.5 72    35 B1 (0.51 0.4 0.069 0.014 0) *
##                                  111059) age< 80.5 154    85 B2 (0.36 0.45 0.11 0.078 0.0065) *
##                               27765) age< 73.5 129    58 B1 (0.55 0.21 0.16 0.07 0.016) *
##                             13883) age< 70.5 297   167 B2 (0.4 0.44 0.12 0.044 0)  
##                               27766) osteoporosis< 0.5 218   125 B1 (0.43 0.41 0.13 0.037 0)  
##                                 55532) reimbursement2008< 2945 194   108 B1 (0.44 0.38 0.14 0.036 0) *
##                                 55533) reimbursement2008>=2945 24     9 B2 (0.29 0.62 0.042 0.042 0) *
##                               27767) osteoporosis>=0.5 79    38 B2 (0.33 0.52 0.089 0.063 0) *
##                          3471) age>=83.5 217   116 B2 (0.35 0.47 0.16 0.028 0) *
##                   217) cancer>=0.5 383   220 B2 (0.37 0.43 0.15 0.044 0.01)  
##                     434) reimbursement2008< 2705 238   136 B1 (0.43 0.36 0.16 0.038 0.013)  
##                       868) depression< 0.5 167    84 B1 (0.5 0.3 0.15 0.042 0.012) *
##                       869) depression>=0.5 71    35 B2 (0.27 0.51 0.18 0.028 0.014) *
##                     435) reimbursement2008>=2705 145    68 B2 (0.27 0.53 0.14 0.055 0.0069) *
##                 109) heart.failure>=0.5 4038  2433 B1 (0.4 0.36 0.17 0.066 0.0074)  
##                   218) kidney< 0.5 2819  1620 B1 (0.43 0.35 0.16 0.065 0.0064)  
##                     436) ihd< 0.5 635   319 B1 (0.5 0.31 0.15 0.041 0.0063) *
##                     437) ihd>=0.5 2184  1301 B1 (0.4 0.36 0.16 0.072 0.0064)  
##                       874) reimbursement2008< 2315 393   202 B1 (0.49 0.34 0.12 0.051 0.0051) *
##                       875) reimbursement2008>=2315 1791  1099 B1 (0.39 0.36 0.17 0.076 0.0067)  
##                        1750) age>=39.5 1752  1066 B1 (0.39 0.36 0.17 0.075 0.0068)  
##                          3500) depression< 0.5 1099   639 B1 (0.42 0.35 0.15 0.069 0.0064)  
##                            7000) age< 95.5 1074   620 B1 (0.42 0.35 0.15 0.069 0.0065)  
##                             14000) copd< 0.5 808   450 B1 (0.44 0.34 0.14 0.075 0.0062) *
##                             14001) copd>=0.5 266   166 B2 (0.36 0.38 0.21 0.049 0.0075)  
##                               28002) reimbursement2008>=2540 192   114 B2 (0.38 0.41 0.15 0.057 0.01)  
##                                 56004) age< 78.5 124    67 B1 (0.46 0.38 0.13 0.032 0)  
##                                  112008) age>=72.5 46    20 B1 (0.57 0.22 0.17 0.043 0) *
##                                  112009) age< 72.5 78    41 B2 (0.4 0.47 0.1 0.026 0) *
##                                 56005) age>=78.5 68    37 B2 (0.22 0.46 0.19 0.1 0.029) *
##                               28003) reimbursement2008< 2540 74    48 B3 (0.32 0.3 0.35 0.027 0)  
##                                 56006) cancer>=0.5 8     0 B2 (0 1 0 0 0) *
##                                 56007) cancer< 0.5 66    40 B3 (0.36 0.21 0.39 0.03 0)  
##                                  112014) alzheimers< 0.5 40    24 B1 (0.4 0.3 0.27 0.025 0) *
##                                  112015) alzheimers>=0.5 26    11 B3 (0.31 0.077 0.58 0.038 0) *
##                            7001) age>=95.5 25    10 B2 (0.24 0.6 0.08 0.08 0) *
##                          3501) depression>=0.5 653   412 B2 (0.35 0.37 0.19 0.084 0.0077)  
##                            7002) reimbursement2008< 2655 303   183 B1 (0.4 0.33 0.2 0.069 0.0033) *
##                            7003) reimbursement2008>=2655 350   208 B2 (0.3 0.41 0.18 0.097 0.011) *
##                        1751) age< 39.5 39    18 B2 (0.15 0.54 0.15 0.15 0) *
##                   219) kidney>=0.5 1219   734 B2 (0.33 0.4 0.19 0.07 0.0098)  
##                     438) reimbursement2008< 2615 613   379 B1 (0.38 0.37 0.18 0.059 0.0098)  
##                       876) osteoporosis>=0.5 180   102 B1 (0.43 0.38 0.13 0.061 0)  
##                        1752) reimbursement2008< 2455 112    56 B1 (0.5 0.29 0.12 0.08 0) *
##                        1753) reimbursement2008>=2455 68    33 B2 (0.32 0.51 0.13 0.029 0) *
##                       877) osteoporosis< 0.5 433   275 B2 (0.36 0.36 0.2 0.058 0.014)  
##                        1754) stroke< 0.5 403   252 B1 (0.37 0.36 0.2 0.05 0.015)  
##                          3508) reimbursement2008< 2585 382   238 B2 (0.37 0.38 0.19 0.047 0.01)  
##                            7016) depression< 0.5 229   136 B1 (0.41 0.36 0.19 0.035 0.0087)  
##                             14032) cancer>=0.5 15     5 B1 (0.67 0.13 0.2 0 0) *
##                             14033) cancer< 0.5 214   131 B1 (0.39 0.37 0.19 0.037 0.0093)  
##                               28066) reimbursement2008< 2515 169    98 B1 (0.42 0.34 0.19 0.036 0.012) *
##                               28067) reimbursement2008>=2515 45    23 B2 (0.27 0.49 0.2 0.044 0) *
##                            7017) depression>=0.5 153    91 B2 (0.32 0.41 0.2 0.065 0.013)  
##                             14034) reimbursement2008>=2545 14     5 B1 (0.64 0.14 0.14 0.071 0) *
##                             14035) reimbursement2008< 2545 139    79 B2 (0.29 0.43 0.2 0.065 0.014) *
##                          3509) reimbursement2008>=2585 21    12 B1 (0.43 0.14 0.24 0.095 0.095) *
##                        1755) stroke>=0.5 30    19 B2 (0.17 0.37 0.3 0.17 0) *
##                     439) reimbursement2008>=2615 606   347 B2 (0.28 0.43 0.2 0.081 0.0099) *
##                55) arthritis>=0.5 3671  2129 B2 (0.37 0.42 0.15 0.057 0.0065)  
##                 110) reimbursement2008< 2665 2068  1224 B1 (0.41 0.4 0.14 0.057 0.0048)  
##                   220) ihd< 0.5 517   274 B1 (0.47 0.37 0.11 0.048 0.0019)  
##                     440) reimbursement2008< 2295 143    57 B1 (0.6 0.26 0.077 0.063 0) *
##                     441) reimbursement2008>=2295 374   217 B1 (0.42 0.41 0.12 0.043 0.0027)  
##                       882) reimbursement2008< 2315 25     6 B2 (0.24 0.76 0 0 0) *
##                       883) reimbursement2008>=2315 349   198 B1 (0.43 0.39 0.13 0.046 0.0029)  
##                        1766) cancer< 0.5 336   186 B1 (0.45 0.38 0.13 0.042 0)  
##                          3532) age< 90.5 322   176 B1 (0.45 0.37 0.13 0.043 0) *
##                          3533) age>=90.5 14     5 B2 (0.29 0.64 0.071 0 0) *
##                        1767) cancer>=0.5 13     6 B2 (0.077 0.54 0.15 0.15 0.077) *
##                   221) ihd>=0.5 1551   925 B2 (0.39 0.4 0.14 0.059 0.0058)  
##                     442) age< 35 18     5 B1 (0.72 0.22 0 0.056 0) *
##                     443) age>=35 1533   911 B2 (0.38 0.41 0.15 0.059 0.0059)  
##                       886) kidney< 0.5 1101   656 B1 (0.4 0.4 0.14 0.052 0.0045)  
##                        1772) stroke< 0.5 1057   623 B1 (0.41 0.4 0.14 0.051 0.0038)  
##                          3544) cancer< 0.5 1008   590 B1 (0.41 0.4 0.13 0.053 0.004)  
##                            7088) reimbursement2008>=2535 275   154 B2 (0.39 0.44 0.12 0.04 0.0036)  
##                             14176) age< 63.5 44    17 B2 (0.32 0.61 0.045 0.023 0) *
##                             14177) age>=63.5 231   137 B1 (0.41 0.41 0.14 0.043 0.0043)  
##                               28354) alzheimers< 0.5 169    95 B1 (0.44 0.36 0.15 0.047 0.0059) *
##                               28355) alzheimers>=0.5 62    28 B2 (0.32 0.55 0.097 0.032 0) *
##                            7089) reimbursement2008< 2535 733   423 B1 (0.42 0.38 0.14 0.057 0.0041)  
##                             14178) age>=97.5 10     3 B1 (0.7 0.2 0.1 0 0) *
##                             14179) age< 97.5 723   420 B1 (0.42 0.38 0.14 0.058 0.0041)  
##                               28358) age< 90.5 689   394 B1 (0.43 0.38 0.13 0.055 0.0044)  
##                                 56716) heart.failure< 0.5 367   198 B1 (0.46 0.36 0.14 0.035 0.0082) *
##                                 56717) heart.failure>=0.5 322   192 B2 (0.39 0.4 0.13 0.078 0)  
##                                  113434) age< 67.5 78    43 B1 (0.45 0.29 0.19 0.064 0) *
##                                  113435) age>=67.5 244   137 B2 (0.37 0.44 0.11 0.082 0) *
##                               28359) age>=90.5 34    18 B2 (0.24 0.47 0.18 0.12 0) *
##                          3545) cancer>=0.5 49    29 B2 (0.33 0.41 0.24 0.02 0) *
##                        1773) stroke>=0.5 44    20 B2 (0.25 0.55 0.11 0.068 0.023) *
##                       887) kidney>=0.5 432   254 B2 (0.33 0.41 0.17 0.079 0.0093)  
##                        1774) reimbursement2008>=2215 403   232 B2 (0.32 0.42 0.17 0.069 0.0099) *
##                        1775) reimbursement2008< 2215 29    16 B1 (0.45 0.24 0.1 0.21 0) *
##                 111) reimbursement2008>=2665 1603   878 B2 (0.32 0.45 0.16 0.058 0.0087) *
##           7) reimbursement2008>=3065 69518 41677 B2 (0.27 0.4 0.2 0.11 0.017)  
##            14) diabetes< 0.5 15717  8966 B1 (0.43 0.35 0.15 0.064 0.0071)  
##              28) cancer< 0.5 13123  7034 B1 (0.46 0.34 0.13 0.058 0.0065)  
##                56) arthritis< 0.5 9625  4692 B1 (0.51 0.31 0.12 0.054 0.0058)  
##                 112) ihd< 0.5 3135  1246 B1 (0.6 0.26 0.095 0.036 0.0032)  
##                   224) depression< 0.5 2292   821 B1 (0.64 0.24 0.08 0.034 0.0044) *
##                   225) depression>=0.5 843   425 B1 (0.5 0.33 0.14 0.04 0)  
##                     450) age< 92.5 810   398 B1 (0.51 0.32 0.13 0.041 0)  
##                       900) reimbursement2008>=11525 117    40 B1 (0.66 0.21 0.068 0.06 0) *
##                       901) reimbursement2008< 11525 693   358 B1 (0.48 0.33 0.14 0.038 0)  
##                        1802) reimbursement2008< 11105 684   352 B1 (0.49 0.34 0.14 0.038 0)  
##                          3604) reimbursement2008< 4365 286   134 B1 (0.53 0.33 0.12 0.017 0) *
##                          3605) reimbursement2008>=4365 398   218 B1 (0.45 0.34 0.15 0.053 0)  
##                            7210) reimbursement2008>=4700 340   173 B1 (0.49 0.31 0.14 0.053 0) *
##                            7211) reimbursement2008< 4700 58    28 B2 (0.22 0.52 0.21 0.052 0) *
##                        1803) reimbursement2008>=11105 9     4 B3 (0.33 0.11 0.56 0 0) *
##                     451) age>=92.5 33    14 B2 (0.18 0.58 0.21 0.03 0) *
##                 113) ihd>=0.5 6490  3446 B1 (0.47 0.33 0.13 0.063 0.0071)  
##                   226) depression< 0.5 4266  2110 B1 (0.51 0.31 0.12 0.056 0.0061)  
##                     452) osteoporosis< 0.5 3304  1572 B1 (0.52 0.3 0.12 0.055 0.0064)  
##                       904) reimbursement2008>=5905 1626   714 B1 (0.56 0.25 0.12 0.061 0.0068) *
##                       905) reimbursement2008< 5905 1678   858 B1 (0.49 0.34 0.12 0.05 0.006)  
##                        1810) reimbursement2008< 5695 1608   814 B1 (0.49 0.33 0.12 0.051 0.0062) *
##                        1811) reimbursement2008>=5695 70    34 B2 (0.37 0.51 0.086 0.029 0) *
##                     453) osteoporosis>=0.5 962   538 B1 (0.44 0.38 0.12 0.057 0.0052)  
##                       906) stroke< 0.5 857   465 B1 (0.46 0.37 0.12 0.056 0.0047)  
##                        1812) heart.failure< 0.5 405   203 B1 (0.5 0.35 0.1 0.044 0.0074)  
##                          3624) age< 83.5 329   159 B1 (0.52 0.32 0.11 0.049 0.0091) *
##                          3625) age>=83.5 76    41 B2 (0.42 0.46 0.092 0.026 0)  
##                            7250) reimbursement2008>=6785 21     7 B1 (0.67 0.24 0.095 0 0) *
##                            7251) reimbursement2008< 6785 55    25 B2 (0.33 0.55 0.091 0.036 0) *
##                        1813) heart.failure>=0.5 452   262 B1 (0.42 0.38 0.13 0.066 0.0022)  
##                          3626) reimbursement2008>=3875 362   201 B1 (0.44 0.35 0.13 0.069 0.0028) *
##                          3627) reimbursement2008< 3875 90    45 B2 (0.32 0.5 0.12 0.056 0)  
##                            7254) age< 69.5 21     9 B1 (0.57 0.29 0.048 0.095 0) *
##                            7255) age>=69.5 69    30 B2 (0.25 0.57 0.14 0.043 0) *
##                       907) stroke>=0.5 105    54 B2 (0.3 0.49 0.13 0.067 0.0095) *
##                   227) depression>=0.5 2224  1336 B1 (0.4 0.35 0.16 0.076 0.009)  
##                     454) kidney< 0.5 1518   863 B1 (0.43 0.34 0.16 0.061 0.0053) *
##                     455) kidney>=0.5 706   440 B2 (0.33 0.38 0.17 0.11 0.017)  
##                       910) reimbursement2008>=3155 696   431 B2 (0.33 0.38 0.16 0.11 0.017)  
##                        1820) heart.failure< 0.5 177    99 B1 (0.44 0.35 0.15 0.062 0) *
##                        1821) heart.failure>=0.5 519   316 B2 (0.3 0.39 0.17 0.12 0.023) *
##                       911) reimbursement2008< 3155 10     4 B3 (0.1 0.1 0.6 0.2 0) *
##                57) arthritis>=0.5 3498  2017 B2 (0.33 0.42 0.17 0.069 0.0083)  
##                 114) reimbursement2008< 8525 2340  1270 B2 (0.31 0.46 0.17 0.062 0.0064)  
##                   228) reimbursement2008< 4645 1359   754 B2 (0.34 0.45 0.15 0.056 0.0059)  
##                     456) ihd< 0.5 440   248 B2 (0.4 0.44 0.11 0.045 0.0045)  
##                       912) reimbursement2008< 3155 58    22 B2 (0.34 0.62 0 0.017 0.017) *
##                       913) reimbursement2008>=3155 382   225 B1 (0.41 0.41 0.13 0.05 0.0026)  
##                        1826) reimbursement2008< 3245 25     8 B1 (0.68 0.28 0.04 0 0) *
##                        1827) reimbursement2008>=3245 357   208 B2 (0.39 0.42 0.13 0.053 0.0028)  
##                          3654) age>=80.5 91    46 B1 (0.49 0.34 0.099 0.066 0) *
##                          3655) age< 80.5 266   148 B2 (0.36 0.44 0.15 0.049 0.0038) *
##                     457) ihd>=0.5 919   506 B2 (0.32 0.45 0.17 0.061 0.0065) *
##                   229) reimbursement2008>=4645 981   516 B2 (0.26 0.47 0.19 0.069 0.0071) *
##                 115) reimbursement2008>=8525 1158   722 B1 (0.38 0.35 0.17 0.085 0.012)  
##                   230) copd< 0.5 714   396 B1 (0.45 0.33 0.13 0.085 0.007)  
##                     460) depression< 0.5 412   196 B1 (0.52 0.29 0.1 0.073 0.0097) *
##                     461) depression>=0.5 302   183 B2 (0.34 0.39 0.16 0.1 0.0033)  
##                       922) age>=92.5 9     3 B1 (0.67 0 0.11 0.11 0.11) *
##                       923) age< 92.5 293   174 B2 (0.33 0.41 0.16 0.1 0)  
##                        1846) stroke>=0.5 39    19 B1 (0.51 0.31 0.1 0.077 0) *
##                        1847) stroke< 0.5 254   147 B2 (0.3 0.42 0.17 0.11 0) *
##                   231) copd>=0.5 444   272 B2 (0.27 0.39 0.24 0.086 0.02)  
##                     462) osteoporosis< 0.5 282   187 B2 (0.31 0.34 0.25 0.082 0.018)  
##                       924) reimbursement2008< 27390 220   143 B1 (0.35 0.3 0.26 0.073 0.018)  
##                        1848) reimbursement2008>=12810 132    78 B1 (0.41 0.32 0.2 0.068 0.0076)  
##                          3696) age< 84.5 105    55 B1 (0.48 0.33 0.15 0.029 0.0095) *
##                          3697) age>=84.5 27    17 B3 (0.15 0.26 0.37 0.22 0) *
##                        1849) reimbursement2008< 12810 88    57 B3 (0.26 0.27 0.35 0.08 0.034) *
##                       925) reimbursement2008>=27390 62    33 B2 (0.18 0.47 0.23 0.11 0.016) *
##                     463) osteoporosis>=0.5 162    85 B2 (0.19 0.48 0.22 0.093 0.025) *
##              29) cancer>=0.5 2594  1539 B2 (0.26 0.41 0.24 0.091 0.01)  
##                58) reimbursement2008< 5770 1000   562 B2 (0.3 0.44 0.19 0.07 0.005) *
##                59) reimbursement2008>=5770 1594   977 B2 (0.23 0.39 0.27 0.1 0.014)  
##                 118) reimbursement2008>=8645 1054   656 B2 (0.27 0.38 0.24 0.1 0.015)  
##                   236) arthritis< 0.5 745   464 B2 (0.31 0.38 0.2 0.097 0.013)  
##                     472) ihd< 0.5 159    94 B1 (0.41 0.32 0.21 0.05 0.013)  
##                       944) reimbursement2008>=11995 76    36 B1 (0.53 0.24 0.16 0.066 0.013) *
##                       945) reimbursement2008< 11995 83    50 B2 (0.3 0.4 0.25 0.036 0.012) *
##                     473) ihd>=0.5 586   356 B2 (0.28 0.39 0.2 0.11 0.014) *
##                   237) arthritis>=0.5 309   192 B2 (0.16 0.38 0.32 0.12 0.019)  
##                     474) reimbursement2008>=10960 237   136 B2 (0.15 0.43 0.3 0.11 0.013)  
##                       948) copd< 0.5 126    64 B2 (0.17 0.49 0.26 0.071 0) *
##                       949) copd>=0.5 111    72 B2 (0.12 0.35 0.35 0.15 0.027)  
##                        1898) age< 75.5 54    30 B3 (0.15 0.26 0.44 0.13 0.019) *
##                        1899) age>=75.5 57    32 B2 (0.088 0.44 0.26 0.18 0.035) *
##                     475) reimbursement2008< 10960 72    44 B3 (0.19 0.22 0.39 0.15 0.042) *
##                 119) reimbursement2008< 8645 540   321 B2 (0.16 0.41 0.32 0.11 0.011)  
##                   238) heart.failure>=0.5 243   128 B2 (0.14 0.47 0.28 0.099 0.016) *
##                   239) heart.failure< 0.5 297   191 B3 (0.18 0.35 0.36 0.11 0.0067)  
##                     478) depression< 0.5 226   141 B2 (0.18 0.38 0.33 0.12 0.0044) *
##                     479) depression>=0.5 71    39 B3 (0.17 0.27 0.45 0.099 0.014) *
##            15) diabetes>=0.5 53801 31450 B2 (0.23 0.42 0.21 0.13 0.02)  
##              30) kidney< 0.5 25067 14311 B2 (0.3 0.43 0.19 0.076 0.0074)  
##                60) arthritis< 0.5 15178  9179 B2 (0.35 0.4 0.17 0.069 0.0063)  
##                 120) cancer< 0.5 12572  7709 B2 (0.39 0.39 0.16 0.063 0.0059)  
##                   240) ihd< 0.5 2617  1376 B1 (0.47 0.34 0.13 0.049 0.0053)  
##                     480) reimbursement2008>=9400 403   171 B1 (0.58 0.21 0.15 0.05 0.0099) *
##                     481) reimbursement2008< 9400 2214  1205 B1 (0.46 0.36 0.13 0.048 0.0045)  
##                       962) osteoporosis< 0.5 1636   847 B1 (0.48 0.34 0.12 0.049 0.0043)  
##                        1924) alzheimers< 0.5 1127   559 B1 (0.5 0.33 0.12 0.042 0.0035) *
##                        1925) alzheimers>=0.5 509   288 B1 (0.43 0.36 0.13 0.065 0.0059)  
##                          3850) reimbursement2008< 3775 137    68 B1 (0.5 0.3 0.12 0.066 0.0073) *
##                          3851) reimbursement2008>=3775 372   220 B1 (0.41 0.39 0.13 0.065 0.0054)  
##                            7702) reimbursement2008>=4055 330   188 B1 (0.43 0.36 0.13 0.07 0.0061)  
##                             15404) reimbursement2008>=4185 309   177 B1 (0.43 0.38 0.12 0.065 0.0065)  
##                               30808) reimbursement2008>=4635 253   138 B1 (0.45 0.35 0.12 0.067 0.0079)  
##                                 61616) age< 96 245   131 B1 (0.47 0.36 0.11 0.065 0.0041)  
##                                  123232) reimbursement2008< 8170 209   106 B1 (0.49 0.33 0.11 0.067 0) *
##                                  123233) reimbursement2008>=8170 36    19 B2 (0.31 0.47 0.14 0.056 0.028)  
##                                    246466) age< 74.5 15     6 B1 (0.6 0.13 0.2 0 0.067) *
##                                    246467) age>=74.5 21     6 B2 (0.095 0.71 0.095 0.095 0) *
##                                 61617) age>=96 8     5 B3 (0.12 0.25 0.38 0.12 0.12) *
##                               30809) reimbursement2008< 4635 56    28 B2 (0.3 0.5 0.14 0.054 0) *
##                             15405) reimbursement2008< 4185 21    11 B1 (0.48 0.095 0.29 0.14 0) *
##                            7703) reimbursement2008< 4055 42    17 B2 (0.24 0.6 0.14 0.024 0) *
##                       963) osteoporosis>=0.5 578   342 B2 (0.38 0.41 0.16 0.047 0.0052)  
##                        1926) depression< 0.5 339   189 B1 (0.44 0.37 0.13 0.047 0.0029)  
##                          3852) reimbursement2008< 4905 211   119 B1 (0.44 0.42 0.11 0.033 0)  
##                            7704) reimbursement2008< 4075 142    71 B1 (0.5 0.36 0.11 0.035 0) *
##                            7705) reimbursement2008>=4075 69    31 B2 (0.3 0.55 0.12 0.029 0) *
##                          3853) reimbursement2008>=4905 128    70 B1 (0.45 0.3 0.17 0.07 0.0078) *
##                        1927) depression>=0.5 239   130 B2 (0.29 0.46 0.2 0.046 0.0084)  
##                          3854) copd< 0.5 181    88 B2 (0.31 0.51 0.13 0.039 0.011) *
##                          3855) copd>=0.5 58    34 B3 (0.24 0.28 0.41 0.069 0) *
##                   241) ihd>=0.5 9955  5976 B2 (0.36 0.4 0.17 0.067 0.006)  
##                     482) depression< 0.5 5563  3339 B1 (0.4 0.38 0.15 0.059 0.0059)  
##                       964) reimbursement2008>=8955 1363   758 B1 (0.44 0.32 0.16 0.067 0.0088)  
##                        1928) copd< 0.5 798   405 B1 (0.49 0.32 0.12 0.064 0.0038) *
##                        1929) copd>=0.5 565   353 B1 (0.38 0.32 0.22 0.073 0.016)  
##                          3858) stroke>=0.5 116    64 B2 (0.35 0.45 0.12 0.06 0.017)  
##                            7716) age>=74.5 63    36 B1 (0.43 0.33 0.16 0.063 0.016) *
##                            7717) age< 74.5 53    22 B2 (0.26 0.58 0.075 0.057 0.019) *
##                          3859) stroke< 0.5 449   278 B1 (0.38 0.28 0.24 0.076 0.016) *
##                       965) reimbursement2008< 8955 4200  2510 B2 (0.39 0.4 0.15 0.056 0.005)  
##                        1930) heart.failure< 0.5 1953  1129 B1 (0.42 0.4 0.13 0.045 0.0041)  
##                          3860) reimbursement2008< 3415 343   172 B1 (0.5 0.37 0.096 0.032 0.0058) *
##                          3861) reimbursement2008>=3415 1610   954 B2 (0.41 0.41 0.14 0.048 0.0037)  
##                            7722) age< 42.5 43    17 B1 (0.6 0.26 0.07 0.07 0) *
##                            7723) age>=42.5 1567   922 B2 (0.4 0.41 0.14 0.047 0.0038)  
##                             15446) age>=50.5 1527   894 B2 (0.4 0.41 0.13 0.047 0.0039)  
##                               30892) reimbursement2008>=3465 1478   870 B2 (0.41 0.41 0.13 0.045 0.0027)  
##                                 61784) reimbursement2008< 4655 759   431 B1 (0.43 0.4 0.12 0.043 0.0013)  
##                                  123568) reimbursement2008>=4315 158    79 B1 (0.5 0.35 0.095 0.051 0.0063) *
##                                  123569) reimbursement2008< 4315 601   352 B1 (0.41 0.41 0.13 0.042 0)  
##                                    247138) reimbursement2008< 4295 592   344 B1 (0.42 0.41 0.13 0.042 0)  
##                                      494276) age>=82.5 135    71 B1 (0.47 0.36 0.15 0.015 0) *
##                                      494277) age< 82.5 457   266 B2 (0.4 0.42 0.13 0.05 0)  
##                                        988554) age< 74.5 290   166 B1 (0.43 0.39 0.13 0.052 0)  
##                                         1977108) age>=62.5 234   128 B1 (0.45 0.38 0.12 0.047 0) *
##                                         1977109) age< 62.5 56    31 B2 (0.32 0.45 0.16 0.071 0) *
##                                        988555) age>=74.5 167    90 B2 (0.36 0.46 0.13 0.048 0)  
##                                         1977110) reimbursement2008>=4105 39    20 B1 (0.49 0.36 0.13 0.026 0) *
##                                         1977111) reimbursement2008< 4105 128    65 B2 (0.32 0.49 0.13 0.055 0) *
##                                    247139) reimbursement2008>=4295 9     1 B2 (0.11 0.89 0 0 0) *
##                                 61785) reimbursement2008>=4655 719   414 B2 (0.38 0.42 0.15 0.047 0.0042)  
##                                  123570) reimbursement2008< 5835 346   180 B2 (0.35 0.48 0.13 0.038 0.0029) *
##                                  123571) reimbursement2008>=5835 373   223 B1 (0.4 0.37 0.16 0.056 0.0054)  
##                                    247142) alzheimers>=0.5 124    64 B1 (0.48 0.31 0.15 0.04 0.0081)  
##                                      494284) reimbursement2008< 8555 114    55 B1 (0.52 0.28 0.16 0.035 0.0088) *
##                                      494285) reimbursement2008>=8555 10     3 B2 (0.1 0.7 0.1 0.1 0) *
##                                    247143) alzheimers< 0.5 249   149 B2 (0.36 0.4 0.17 0.064 0.004)  
##                                      494286) reimbursement2008>=6045 217   124 B2 (0.36 0.43 0.14 0.069 0.0046) *
##                                      494287) reimbursement2008< 6045 32    20 B1 (0.38 0.22 0.38 0.031 0)  
##                                        988574) age< 72.5 11     4 B1 (0.64 0.18 0.18 0 0) *
##                                        988575) age>=72.5 21    11 B3 (0.24 0.24 0.48 0.048 0) *
##                               30893) reimbursement2008< 3465 49    24 B2 (0.27 0.51 0.082 0.1 0.041) *
##                             15447) age< 50.5 40    26 B1 (0.35 0.3 0.3 0.05 0) *
##                        1931) heart.failure>=0.5 2247  1339 B2 (0.35 0.4 0.17 0.066 0.0058)  
##                          3862) reimbursement2008>=5335 866   530 B1 (0.39 0.37 0.16 0.074 0.0058)  
##                            7724) reimbursement2008>=8115 129    68 B2 (0.36 0.47 0.12 0.047 0) *
##                            7725) reimbursement2008< 8115 737   447 B1 (0.39 0.35 0.17 0.079 0.0068)  
##                             15450) age< 94.5 703   421 B1 (0.4 0.35 0.17 0.075 0.0071)  
##                               30900) reimbursement2008>=6635 298   164 B1 (0.45 0.32 0.15 0.067 0.013) *
##                               30901) reimbursement2008< 6635 405   255 B2 (0.37 0.37 0.18 0.081 0.0025)  
##                                 61802) reimbursement2008< 5685 137    80 B1 (0.42 0.31 0.16 0.11 0) *
##                                 61803) reimbursement2008>=5685 268   161 B2 (0.34 0.4 0.19 0.067 0.0037) *
##                             15451) age>=94.5 34    17 B2 (0.24 0.5 0.12 0.15 0) *
##                          3863) reimbursement2008< 5335 1381   795 B2 (0.33 0.42 0.18 0.061 0.0058)  
##                            7726) copd< 0.5 997   591 B2 (0.36 0.41 0.17 0.057 0.006)  
##                             15452) age< 69.5 297   171 B1 (0.42 0.38 0.15 0.04 0.0034)  
##                               30904) reimbursement2008< 5065 274   153 B1 (0.44 0.36 0.15 0.04 0.0036)  
##                                 61808) alzheimers< 0.5 174    91 B1 (0.48 0.3 0.17 0.046 0.0057) *
##                                 61809) alzheimers>=0.5 100    54 B2 (0.38 0.46 0.13 0.03 0)  
##                                  123618) reimbursement2008>=4355 26    10 B1 (0.62 0.15 0.15 0.077 0) *
##                                  123619) reimbursement2008< 4355 74    32 B2 (0.3 0.57 0.12 0.014 0) *
##                               30905) reimbursement2008>=5065 23     9 B2 (0.22 0.61 0.13 0.043 0) *
##                             15453) age>=69.5 700   407 B2 (0.33 0.42 0.18 0.064 0.0071) *
##                            7727) copd>=0.5 384   204 B2 (0.27 0.47 0.19 0.07 0.0052) *
##                     483) depression>=0.5 4392  2538 B2 (0.31 0.42 0.18 0.077 0.0061)  
##                       966) reimbursement2008< 8325 2928  1619 B2 (0.31 0.45 0.17 0.065 0.0065)  
##                        1932) copd< 0.5 1987  1102 B2 (0.33 0.45 0.16 0.056 0.0045)  
##                          3864) age< 98.5 1964  1085 B2 (0.33 0.45 0.16 0.057 0.0046)  
##                            7728) reimbursement2008< 3085 22     8 B1 (0.64 0.23 0.045 0.091 0) *
##                            7729) reimbursement2008>=3085 1942  1068 B2 (0.33 0.45 0.16 0.056 0.0046)  
##                             15458) heart.failure< 0.5 889   508 B2 (0.37 0.43 0.15 0.051 0.0034) *
##                             15459) heart.failure>=0.5 1053   560 B2 (0.3 0.47 0.17 0.061 0.0057)  
##                               30918) osteoporosis< 0.5 721   396 B2 (0.32 0.45 0.16 0.061 0.0055)  
##                                 61836) age>=86.5 109    59 B1 (0.46 0.3 0.15 0.083 0.0092) *
##                                 61837) age< 86.5 612   320 B2 (0.3 0.48 0.16 0.057 0.0049) *
##                               30919) osteoporosis>=0.5 332   164 B2 (0.24 0.51 0.18 0.06 0.006) *
##                          3865) age>=98.5 23    12 B3 (0.26 0.26 0.48 0 0) *
##                        1933) copd>=0.5 941   517 B2 (0.26 0.45 0.2 0.084 0.011) *
##                       967) reimbursement2008>=8325 1464   919 B2 (0.32 0.37 0.2 0.1 0.0055)  
##                        1934) reimbursement2008< 8485 36    16 B1 (0.56 0.22 0.22 0 0) *
##                        1935) reimbursement2008>=8485 1428   891 B2 (0.32 0.38 0.2 0.1 0.0056)  
##                          3870) age< 78.5 837   532 B2 (0.35 0.36 0.19 0.098 0.0036)  
##                            7740) reimbursement2008< 21320 639   406 B1 (0.36 0.35 0.19 0.092 0.0031)  
##                             15480) age< 49.5 83    47 B2 (0.35 0.43 0.096 0.12 0) *
##                             15481) age>=49.5 556   352 B1 (0.37 0.33 0.21 0.088 0.0036)  
##                               30962) age>=67.5 368   230 B1 (0.38 0.36 0.18 0.087 0)  
##                                 61924) reimbursement2008>=10440 261   153 B1 (0.41 0.33 0.17 0.084 0)  
##                                  123848) reimbursement2008< 12585 92    46 B1 (0.5 0.25 0.18 0.065 0) *
##                                  123849) reimbursement2008>=12585 169   105 B2 (0.37 0.38 0.16 0.095 0)  
##                                    247698) reimbursement2008>=14485 109    63 B1 (0.42 0.31 0.17 0.092 0)  
##                                      495396) osteoporosis< 0.5 72    37 B1 (0.49 0.24 0.17 0.11 0) *
##                                      495397) osteoporosis>=0.5 37    20 B2 (0.3 0.46 0.19 0.054 0) *
##                                    247699) reimbursement2008< 14485 60    30 B2 (0.27 0.5 0.13 0.1 0) *
##                                 61925) reimbursement2008< 10440 107    62 B2 (0.28 0.42 0.21 0.093 0) *
##                               30963) age< 67.5 188   122 B1 (0.35 0.28 0.27 0.09 0.011)  
##                                 61926) age>=55.5 135    82 B1 (0.39 0.25 0.27 0.089 0) *
##                                 61927) age< 55.5 53    34 B2 (0.25 0.36 0.26 0.094 0.038) *
##                            7741) reimbursement2008>=21320 198   114 B2 (0.3 0.42 0.16 0.12 0.0051) *
##                          3871) age>=78.5 591   359 B2 (0.28 0.39 0.21 0.11 0.0085)  
##                            7742) heart.failure< 0.5 122    73 B1 (0.4 0.32 0.18 0.098 0)  
##                             15484) reimbursement2008< 11560 40    17 B1 (0.58 0.2 0.18 0.05 0) *
##                             15485) reimbursement2008>=11560 82    51 B2 (0.32 0.38 0.18 0.12 0) *
##                            7743) heart.failure>=0.5 469   276 B2 (0.24 0.41 0.22 0.11 0.011) *
##                 121) cancer>=0.5 2606  1470 B2 (0.21 0.44 0.25 0.098 0.0081) *
##                61) arthritis>=0.5 9889  5132 B2 (0.22 0.48 0.21 0.088 0.0092)  
##                 122) depression< 0.5 5134  2665 B2 (0.25 0.48 0.18 0.08 0.0078)  
##                   244) cancer< 0.5 4305  2260 B2 (0.27 0.48 0.17 0.076 0.0086)  
##                     488) reimbursement2008>=9880 1063   636 B2 (0.32 0.4 0.18 0.089 0.012)  
##                       976) ihd< 0.5 102    49 B1 (0.52 0.27 0.13 0.069 0.0098) *
##                       977) ihd>=0.5 961   562 B2 (0.29 0.42 0.19 0.092 0.012) *
##                     489) reimbursement2008< 9880 3242  1624 B2 (0.25 0.5 0.17 0.072 0.0074) *
##                   245) cancer>=0.5 829   405 B2 (0.15 0.51 0.23 0.1 0.0036) *
##                 123) depression>=0.5 4755  2467 B2 (0.18 0.48 0.23 0.096 0.011) *
##              31) kidney>=0.5 28734 17139 B2 (0.16 0.4 0.23 0.17 0.03)  
##                62) reimbursement2008< 15395 16249  9131 B2 (0.19 0.44 0.24 0.12 0.016)  
##                 124) arthritis< 0.5 9424  5647 B2 (0.23 0.4 0.23 0.12 0.017)  
##                   248) cancer< 0.5 7786  4711 B2 (0.25 0.39 0.21 0.12 0.017)  
##                     496) ihd< 0.5 964   608 B1 (0.37 0.36 0.17 0.085 0.011)  
##                       992) depression< 0.5 572   338 B1 (0.41 0.32 0.16 0.1 0.01)  
##                        1984) reimbursement2008< 3545 101    53 B2 (0.33 0.48 0.15 0.04 0.0099) *
##                        1985) reimbursement2008>=3545 471   270 B1 (0.43 0.29 0.16 0.11 0.011)  
##                          3970) osteoporosis< 0.5 346   186 B1 (0.46 0.27 0.15 0.11 0.014) *
##                          3971) osteoporosis>=0.5 125    82 B2 (0.33 0.34 0.2 0.13 0)  
##                            7942) age>=62 106    67 B1 (0.37 0.37 0.15 0.11 0)  
##                             15884) age>=67.5 93    55 B2 (0.34 0.41 0.16 0.086 0)  
##                               31768) reimbursement2008>=6110 44    23 B1 (0.48 0.3 0.11 0.11 0)  
##                                 63536) reimbursement2008< 9180 26     9 B1 (0.65 0.15 0.077 0.12 0) *
##                                 63537) reimbursement2008>=9180 18     9 B2 (0.22 0.5 0.17 0.11 0) *
##                               31769) reimbursement2008< 6110 49    24 B2 (0.22 0.51 0.2 0.061 0) *
##                             15885) age< 67.5 13     6 B1 (0.54 0.077 0.077 0.31 0) *
##                            7943) age< 62 19    10 B3 (0.11 0.21 0.47 0.21 0) *
##                       993) depression>=0.5 392   227 B2 (0.31 0.42 0.19 0.064 0.013)  
##                        1986) reimbursement2008>=14460 9     2 B1 (0.78 0.22 0 0 0) *
##                        1987) reimbursement2008< 14460 383   220 B2 (0.3 0.43 0.2 0.065 0.013) *
##                     497) ihd>=0.5 6822  4095 B2 (0.24 0.4 0.22 0.12 0.018)  
##                       994) reimbursement2008< 6325 3172  1786 B2 (0.22 0.44 0.22 0.11 0.016) *
##                       995) reimbursement2008>=6325 3650  2309 B2 (0.25 0.37 0.22 0.14 0.019)  
##                        1990) osteoporosis< 0.5 2424  1594 B2 (0.27 0.34 0.23 0.14 0.02)  
##                          3980) depression< 0.5 1234   816 B2 (0.3 0.34 0.2 0.13 0.024)  
##                            7960) reimbursement2008>=12135 349   226 B1 (0.35 0.3 0.16 0.16 0.032)  
##                             15920) age>=54 331   210 B1 (0.37 0.28 0.16 0.16 0.03) *
##                             15921) age< 54 18     9 B2 (0.11 0.5 0.11 0.22 0.056) *
##                            7961) reimbursement2008< 12135 885   570 B2 (0.28 0.36 0.22 0.12 0.021) *
##                          3981) depression>=0.5 1190   778 B2 (0.24 0.35 0.25 0.15 0.016)  
##                            7962) copd< 0.5 547   367 B2 (0.28 0.33 0.25 0.12 0.022)  
##                             15924) reimbursement2008>=9205 310   209 B1 (0.33 0.32 0.21 0.12 0.029)  
##                               31848) reimbursement2008< 9955 50    28 B2 (0.42 0.44 0.02 0.12 0) *
##                               31849) reimbursement2008>=9955 260   180 B1 (0.31 0.3 0.24 0.12 0.035)  
##                                 63698) reimbursement2008>=14765 20     9 B1 (0.55 0.25 0.1 0.05 0.05) *
##                                 63699) reimbursement2008< 14765 240   168 B2 (0.29 0.3 0.25 0.12 0.033)  
##                                  127398) age>=61.5 201   138 B1 (0.31 0.29 0.24 0.13 0.02)  
##                                    254796) reimbursement2008< 12625 112    69 B1 (0.38 0.24 0.28 0.089 0.0089) *
##                                    254797) reimbursement2008>=12625 89    58 B2 (0.22 0.35 0.2 0.19 0.034) *
##                                  127399) age< 61.5 39    25 B2 (0.15 0.36 0.31 0.077 0.1) *
##                             15925) reimbursement2008< 9205 237   156 B2 (0.22 0.34 0.3 0.12 0.013)  
##                               31850) age< 67.5 56    29 B2 (0.2 0.48 0.16 0.16 0) *
##                               31851) age>=67.5 181   118 B3 (0.23 0.3 0.35 0.1 0.017)  
##                                 63702) reimbursement2008>=6865 136    82 B3 (0.25 0.26 0.4 0.074 0.015) *
##                                 63703) reimbursement2008< 6865 45    27 B2 (0.18 0.4 0.2 0.2 0.022) *
##                            7963) copd>=0.5 643   411 B2 (0.21 0.36 0.25 0.17 0.011) *
##                        1991) osteoporosis>=0.5 1226   715 B2 (0.21 0.42 0.22 0.14 0.017) *
##                   249) cancer>=0.5 1638   936 B2 (0.13 0.43 0.29 0.14 0.016) *
##                 125) arthritis>=0.5 6825  3484 B2 (0.13 0.49 0.25 0.12 0.014) *
##                63) reimbursement2008>=15395 12485  8008 B2 (0.13 0.36 0.23 0.24 0.049)  
##                 126) arthritis>=0.5 5402  3220 B2 (0.094 0.4 0.24 0.22 0.04)  
##                   252) reimbursement2008< 34925 3345  1942 B2 (0.11 0.42 0.25 0.19 0.03)  
##                     504) depression< 0.5 1291   714 B2 (0.14 0.45 0.22 0.17 0.025)  
##                      1008) cancer< 0.5 973   546 B2 (0.16 0.44 0.19 0.18 0.029) *
##                      1009) cancer>=0.5 318   168 B2 (0.072 0.47 0.3 0.14 0.013)  
##                        2018) reimbursement2008>=16525 293   144 B2 (0.068 0.51 0.28 0.13 0.014) *
##                        2019) reimbursement2008< 16525 25    10 B3 (0.12 0.04 0.6 0.24 0) *
##                     505) depression>=0.5 2054  1228 B2 (0.092 0.4 0.27 0.2 0.034) *
##                   253) reimbursement2008>=34925 2057  1278 B2 (0.067 0.38 0.23 0.27 0.055)  
##                     506) copd< 0.5 520   300 B2 (0.096 0.42 0.23 0.21 0.042) *
##                     507) copd>=0.5 1537   978 B2 (0.057 0.36 0.23 0.29 0.06)  
##                      1014) age>=62.5 1286   804 B2 (0.058 0.37 0.24 0.27 0.061) *
##                      1015) age< 62.5 251   153 B4 (0.052 0.31 0.2 0.39 0.052)  
##                        2030) reimbursement2008< 101585 237   150 B4 (0.055 0.32 0.21 0.37 0.051)  
##                          4060) cancer>=0.5 62    36 B2 (0.048 0.42 0.27 0.24 0.016) *
##                          4061) cancer< 0.5 175   103 B4 (0.057 0.29 0.18 0.41 0.063) *
##                        2031) reimbursement2008>=101585 14     3 B4 (0 0.071 0.071 0.79 0.071) *
##                 127) arthritis< 0.5 7083  4788 B2 (0.15 0.32 0.22 0.25 0.057)  
##                   254) cancer< 0.5 5298  3651 B2 (0.17 0.31 0.2 0.26 0.062)  
##                     508) depression< 0.5 2489  1797 B2 (0.22 0.28 0.18 0.27 0.06)  
##                      1016) copd>=0.5 1317   890 B2 (0.2 0.32 0.18 0.24 0.056)  
##                        2032) ihd< 0.5 72    41 B1 (0.43 0.25 0.15 0.11 0.056) *
##                        2033) ihd>=0.5 1245   836 B2 (0.19 0.33 0.18 0.24 0.056) *
##                      1017) copd< 0.5 1172   815 B4 (0.23 0.23 0.17 0.3 0.065)  
##                        2034) reimbursement2008>=43640 191   129 B2 (0.15 0.32 0.23 0.22 0.073)  
##                          4068) age>=64.5 172   112 B2 (0.16 0.35 0.2 0.23 0.064) *
##                          4069) age< 64.5 19    10 B3 (0.11 0.11 0.47 0.16 0.16) *
##                        2035) reimbursement2008< 43640 981   666 B4 (0.25 0.21 0.16 0.32 0.063)  
##                          4070) reimbursement2008< 23175 468   337 B1 (0.28 0.24 0.16 0.26 0.056)  
##                            8140) age< 93.5 457   326 B1 (0.29 0.23 0.16 0.26 0.057)  
##                             16280) age< 86.5 398   288 B1 (0.28 0.25 0.16 0.25 0.063)  
##                               32560) alzheimers>=0.5 179   122 B1 (0.32 0.28 0.15 0.17 0.073)  
##                                 65120) reimbursement2008>=21440 38    19 B2 (0.24 0.5 0.11 0.13 0.026) *
##                                 65121) reimbursement2008< 21440 141    93 B1 (0.34 0.23 0.16 0.18 0.085)  
##                                  130242) reimbursement2008>=17585 89    53 B1 (0.4 0.17 0.19 0.16 0.079) *
##                                  130243) reimbursement2008< 17585 52    35 B2 (0.23 0.33 0.12 0.23 0.096) *
##                               32561) alzheimers< 0.5 219   151 B4 (0.24 0.23 0.16 0.31 0.055) *
##                             16281) age>=86.5 59    38 B1 (0.36 0.1 0.17 0.36 0.017)  
##                               32562) reimbursement2008>=19680 18     8 B1 (0.56 0.11 0 0.28 0.056) *
##                               32563) reimbursement2008< 19680 41    25 B4 (0.27 0.098 0.24 0.39 0) *
##                            8141) age>=93.5 11     6 B2 (0 0.45 0.36 0.18 0) *
##                          4071) reimbursement2008>=23175 513   320 B4 (0.22 0.18 0.16 0.38 0.07) *
##                     509) depression>=0.5 2809  1854 B2 (0.13 0.34 0.22 0.25 0.063) *
##                   255) cancer>=0.5 1785  1137 B2 (0.097 0.36 0.28 0.22 0.041) *
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 171981  12159    140    186      0
##        B2  26534  25373    156    196      0
##        B3  12021  12119    286    160      0
##        B4   4652   6824     70    360      0
##        B5    483   1029     14     60      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.7205162             NA      0.7188342      0.7221934      0.6712663 
## AccuracyPValue  McnemarPValue 
##      0.0000000      0.0000000
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 114141   8610    124    103      0
##        B2  18409  16102    187    142      0
##        B3   8027   8146    118     99      0
##        B4   3099   4584     53    201      0
##        B5    351    657      4     45      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.126669e-01             NA   7.105887e-01   7.147384e-01   6.712700e-01 
## AccuracyPValue  McnemarPValue 
##  7.015238e-319   0.000000e+00
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##                      model_id model_method
## 1 All.X.lser.ys.cp.4015.rpart        rpart
##                                                                                                                                             feats
## 1 age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1               1                     75.838                16.151
##   max.Accuracy.fit max.AccuracyLower.fit max.AccuracyUpper.fit
## 1        0.7205162             0.7188342             0.7221934
##   max.Kappa.fit min.loss.error.fit max.Accuracy.OOB max.AccuracyLower.OOB
## 1            NA          0.7582887        0.7126669             0.7105887
##   max.AccuracyUpper.OOB max.Kappa.OOB min.loss.error.OOB min.SSE.fit
## 1             0.7147384            NA          0.7578902           0
##   min.loss.errorSD.fit
## 1          0.005993538
## [1] "iterating over method:rf"
## [1] "fitting model: All.X.lser.no.cp.opt.rf"
## [1] "    indep_vars: age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008, .rnorm"
## Loading required package: randomForest
## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.

## + : mtry= 2 
## - : mtry= 2 
## + : mtry= 8 
## - : mtry= 8 
## + : mtry=15 
## - : mtry=15 
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 2 on full training set
## Warning in myfit_mdl_fn(model_id = paste0(model_id_pfx,
## ".lser.no.cp.opt"), : model's bestTune found at an extreme of tuneGrid for
## parameter: mtry

##                 Length  Class      Mode     
## call                  4 -none-     call     
## type                  1 -none-     character
## predicted        274803 factor     numeric  
## err.rate           3000 -none-     numeric  
## confusion            30 -none-     numeric  
## votes           1374015 matrix     numeric  
## oob.times        274803 -none-     numeric  
## classes               5 -none-     character
## importance           15 -none-     numeric  
## importanceSD          0 -none-     NULL     
## localImportance       0 -none-     NULL     
## proximity             0 -none-     NULL     
## ntree                 1 -none-     numeric  
## mtry                  1 -none-     numeric  
## forest               14 -none-     list     
## y                274803 factor     numeric  
## test                  0 -none-     NULL     
## inbag                 0 -none-     NULL     
## xNames               15 -none-     character
## problemType           1 -none-     character
## tuneValue             1 data.frame list     
## obsLevels             5 -none-     character
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 173343  11123      0      0      0
##        B2  27770  24489      0      0      0
##        B3  12454  12124      8      0      0
##        B4   4772   7134      0      0      0
##        B5    497   1089      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.7199339             NA      0.7182509      0.7216123      0.6712663 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 115110   7868      0      0      0
##        B2  19054  15786      0      0      0
##        B3   8252   8138      0      0      0
##        B4   3213   4724      0      0      0
##        B5    357    700      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.7144900             NA      0.7124157      0.7165575      0.6712700 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN 
##                  model_id model_method
## 1 All.X.lser.no.cp.opt.rf           rf
##                                                                                                                                                     feats
## 1 age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008, .rnorm
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1               3                    3479.97               449.343
##   max.Accuracy.fit max.AccuracyLower.fit max.AccuracyUpper.fit
## 1        0.7139405             0.7182509             0.7216123
##   max.Kappa.fit max.Accuracy.OOB max.AccuracyLower.OOB
## 1     0.3281909          0.71449             0.7124157
##   max.AccuracyUpper.OOB max.Kappa.OOB min.SSE.fit
## 1             0.7165575            NA           0
## [1] "fitting model: All.X.lser.ys.cp.opt.rf"
## [1] "    indep_vars: age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008, .rnorm"
## Warning in train.default(x, y, weights = w, ...): The metric "loss.error"
## was not in the result set. Accuracy will be used instead.

## + : mtry= 2 
## - : mtry= 2 
## + : mtry= 8 
## - : mtry= 8 
## + : mtry=15 
## - : mtry=15 
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 15 on full training set
## Warning in myfit_mdl_fn(model_id = paste0(model_id_pfx,
## ".lser.ys.cp.opt"), : model's bestTune found at an extreme of tuneGrid for
## parameter: mtry

##                 Length  Class      Mode     
## call                  4 -none-     call     
## type                  1 -none-     character
## predicted        274803 factor     numeric  
## err.rate           3000 -none-     numeric  
## confusion            30 -none-     numeric  
## votes           1374015 matrix     numeric  
## oob.times        274803 -none-     numeric  
## classes               5 -none-     character
## importance           15 -none-     numeric  
## importanceSD          0 -none-     NULL     
## localImportance       0 -none-     NULL     
## proximity             0 -none-     NULL     
## ntree                 1 -none-     numeric  
## mtry                  1 -none-     numeric  
## forest               14 -none-     list     
## y                274803 factor     numeric  
## test                  0 -none-     NULL     
## inbag                 0 -none-     NULL     
## xNames               15 -none-     character
## problemType           1 -none-     character
## tuneValue             1 data.frame list     
## obsLevels             5 -none-     character
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 184466      0      0      0      0
##        B2      0  52259      0      0      0
##        B3      0      0  24586      0      0
##        B4      0      0      0  11906      0
##        B5      0      0      0      0   1586
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      1.0000000             NA      0.9999866      1.0000000      0.6712663 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 111655   9615   1279    416     13
##        B2  18013  13796   2216    796     19
##        B3   8029   6719   1167    465     10
##        B4   3148   3538    718    513     20
##        B5    360    458    122    114      3
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.939553e-01             NA   6.918392e-01   6.960652e-01   6.712700e-01 
## AccuracyPValue  McnemarPValue 
##   2.423558e-96   0.000000e+00
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow

##                  model_id model_method
## 1 All.X.lser.ys.cp.opt.rf           rf
##                                                                                                                                                     feats
## 1 age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008, .rnorm
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1               3                   4381.707              1409.455
##   max.Accuracy.fit max.AccuracyLower.fit max.AccuracyUpper.fit
## 1                1             0.9999866                     1
##   max.Kappa.fit min.loss.error.fit max.Accuracy.OOB max.AccuracyLower.OOB
## 1            NA                  0        0.6939553             0.6918392
##   max.AccuracyUpper.OOB max.Kappa.OOB min.loss.error.OOB min.SSE.fit
## 1             0.6960652            NA          0.7655102           0
##   min.Accuracy.fit min.Kappa.fit
## 1        0.6943265     0.3128527
# Simplify a model
# fit_df <- glb_entity_df; glb_mdl <- step(<complex>_mdl)

print(glb_models_df)
##                       model_id     model_method
## 1    Baseline.mybaseln_classfr mybaseln_classfr
## 2            MFO.myMFO_classfr    myMFO_classfr
## 3      Random.myrandom_classfr myrandom_classfr
## 4         Max.cor.Y.cv.0.rpart            rpart
## 5         Max.cor.Y.cv.G.rpart            rpart
## 6    Interact.High.cor.y.rpart            rpart
## 7              Low.cor.X.rpart            rpart
## 8   All.X.lser.no.cp.opt.rpart            rpart
## 9  All.X.lser.no.cp.4015.rpart            rpart
## 10  All.X.lser.ys.cp.opt.rpart            rpart
## 11 All.X.lser.ys.cp.4015.rpart            rpart
## 12     All.X.lser.no.cp.opt.rf               rf
## 13     All.X.lser.ys.cp.opt.rf               rf
##                                                                                                                                                      feats
## 1                                                                                                                                  bucket2008.fctr, .rnorm
## 2                                                                                                                                                   .rnorm
## 3                                                                                                                                                   .rnorm
## 4                                                                                                                                               bucket2008
## 5                                                                                                                                               bucket2008
## 6                                                                                                                            bucket2008, reimbursement2008
## 7                             bucket2008, ihd, diabetes, kidney, heart.failure, copd, depression, alzheimers, arthritis, cancer, osteoporosis, stroke, age
## 8          age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008
## 9          age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008
## 10         age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008
## 11         age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008
## 12 age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008, .rnorm
## 13 age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008, .rnorm
##    max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1                0                      0.626                 0.006
## 2                0                      0.515                 0.038
## 3                0                      0.375                 0.035
## 4                0                      4.811                 3.430
## 5                3                     20.801                 3.333
## 6                3                     31.301                 5.050
## 7                3                    101.182                13.937
## 8                3                    107.610                15.544
## 9                1                     73.967                15.630
## 10               3                    108.644                15.505
## 11               1                     75.838                16.151
## 12               3                   3479.970               449.343
## 13               3                   4381.707              1409.455
##    max.Accuracy.fit max.AccuracyLower.fit max.AccuracyUpper.fit
## 1         0.6829729             0.6812294             0.6847125
## 2         0.6712663             0.6695064             0.6730227
## 3         0.4967267             0.4948556             0.4985980
## 4         0.6712663             0.6695064             0.6730227
## 5         0.7031401             0.7014279             0.7048479
## 6         0.7029800             0.7018289             0.7052475
## 7         0.7086058             0.7063706             0.7097740
## 8         0.7086604             0.7064253             0.7098285
## 9         0.7117062             0.7188342             0.7221934
## 10        0.7035404             0.7018289             0.7052475
## 11        0.7205162             0.7188342             0.7221934
## 12        0.7139405             0.7182509             0.7216123
## 13        1.0000000             0.9999866             1.0000000
##    max.Kappa.fit max.Accuracy.OOB max.AccuracyLower.OOB
## 1             NA        0.6838135             0.6816786
## 2             NA        0.6712700             0.6691135
## 3             NA        0.4948800             0.4925879
## 4             NA        0.6712700             0.6691135
## 5      0.3440289        0.7040207             0.7019245
## 6      0.3368565        0.7039770             0.7018807
## 7      0.3161651        0.7077434             0.7056548
## 8      0.3140169        0.7074814             0.7053922
## 9      0.3328722        0.7126669             0.7105887
## 10            NA        0.7039770             0.7018807
## 11            NA        0.7126669             0.7105887
## 12     0.3281909        0.7144900             0.7124157
## 13            NA        0.6939553             0.6918392
##    max.AccuracyUpper.OOB max.Kappa.OOB min.SSE.fit max.AccuracySD.fit
## 1              0.6859425            NA           0                 NA
## 2              0.6734210            NA           0                 NA
## 3              0.4971722            NA           0                 NA
## 4              0.6734210            NA           0                 NA
## 5              0.7061105            NA           0       0.0016657885
## 6              0.7060669            NA           0       0.0016900317
## 7              0.7098254            NA           0       0.0009742811
## 8              0.7095639            NA           0       0.0010140545
## 9              0.7147384            NA           0       0.0021496212
## 10             0.7060669            NA           0                 NA
## 11             0.7147384            NA           0                 NA
## 12             0.7165575            NA           0                 NA
## 13             0.6960652            NA           0                 NA
##    max.KappaSD.fit min.loss.error.fit min.loss.error.OOB
## 1               NA                 NA                 NA
## 2               NA                 NA                 NA
## 3               NA                 NA                 NA
## 4               NA                 NA                 NA
## 5      0.004508795                 NA                 NA
## 6      0.006361514                 NA                 NA
## 7      0.004868262                 NA                 NA
## 8      0.004809558                 NA                 NA
## 9      0.007338010                 NA                 NA
## 10              NA          0.7784778          0.7441403
## 11              NA          0.7582887          0.7578902
## 12              NA                 NA                 NA
## 13              NA          0.0000000          0.7655102
##    min.loss.errorSD.fit min.Accuracy.fit min.Kappa.fit
## 1                    NA               NA            NA
## 2                    NA               NA            NA
## 3                    NA               NA            NA
## 4                    NA               NA            NA
## 5                    NA               NA            NA
## 6                    NA               NA            NA
## 7                    NA               NA            NA
## 8                    NA               NA            NA
## 9                    NA               NA            NA
## 10          0.016425281               NA            NA
## 11          0.005993538               NA            NA
## 12                   NA               NA            NA
## 13                   NA        0.6943265     0.3128527
if (!is.null(glb_model_metric_smmry)) {
    stats_df <- glb_models_df[, "model_id", FALSE]

    stats_mdl_df <- data.frame()
    for (model_id in stats_df$model_id) {
        stats_mdl_df <- rbind(stats_mdl_df, 
            mypredict_mdl(glb_models_lst[[model_id]], glb_entity_df, glb_rsp_var, 
                          glb_rsp_var_out, model_id, "fit",
                                glb_model_metric_smmry, glb_model_metric, 
                                glb_model_metric_maximize, ret_type="stats"))
    }
    stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
    
    stats_mdl_df <- data.frame()
    for (model_id in stats_df$model_id) {
        stats_mdl_df <- rbind(stats_mdl_df, 
            mypredict_mdl(glb_models_lst[[model_id]], glb_newent_df, glb_rsp_var, 
                          glb_rsp_var_out, model_id, "OOB",
                                glb_model_metric_smmry, glb_model_metric, 
                                glb_model_metric_maximize, ret_type="stats"))
    }
    stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
    
#     tmp_models_df <- orderBy(~model_id, glb_models_df)
#     rownames(tmp_models_df) <- seq(1, nrow(tmp_models_df))
#     all.equal(subset(tmp_models_df[, names(stats_df)], model_id != "Random.myrandom_classfr"),
#               subset(stats_df, model_id != "Random.myrandom_classfr"))
#     print(subset(tmp_models_df[, names(stats_df)], model_id != "Random.myrandom_classfr")[, c("model_id", "max.Accuracy.fit")])
#     print(subset(stats_df, model_id != "Random.myrandom_classfr")[, c("model_id", "max.Accuracy.fit")])

    print("Merging following data into glb_models_df:")
    print(stats_mrg_df <- stats_df[, c(1, grep(glb_model_metric, names(stats_df)))])
    print(tmp_models_df <- orderBy(~model_id, glb_models_df[, c("model_id", grep(glb_model_metric, names(stats_df), value=TRUE))]))

    tmp2_models_df <- glb_models_df[, c("model_id", setdiff(names(glb_models_df), grep(glb_model_metric, names(stats_df), value=TRUE)))]
    tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
    print(tmp3_models_df)
    print(names(tmp3_models_df))
    print(glb_models_df <- subset(tmp3_models_df, select=-model_id.1))
}
## [1] "in Baseline.Classifier$predict"
## [1] "class(newdata):"
## [1] "matrix"
## [1] "head(newdata):"
##    bucket2008.fctrB2 bucket2008.fctrB3 bucket2008.fctrB4 bucket2008.fctrB5
## 1                  0                 0                 0                 0
## 2                  0                 0                 0                 0
## 4                  0                 0                 0                 0
## 7                  0                 0                 0                 0
## 11                 0                 0                 0                 0
## 13                 0                 0                 0                 0
##        .rnorm
## 1  -0.9248001
## 2   0.1533902
## 4  -0.9917587
## 7  -0.5160354
## 11 -1.0376029
## 13 -0.3801495
## [1] "x_names: "
## [1] "bucket2008.fctrB2" "bucket2008.fctrB3" "bucket2008.fctrB4"
## [4] "bucket2008.fctrB5"
## [1] "x_vals: "
## [1] "B1" "B2" "B3" "B4" "B5"
## [1] "length(y):"
## [1] 274803
## [1] "head(y):"
## [1] B1 B1 B1 B1 B1 B1
## Levels: B1 B2 B3 B4 B5
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 164967  11747   5214   2275    263
##        B2  24001  16172   6877   4367    842
##        B3  10679   6848   4004   2552    503
##        B4   4020   2835   2081   2399    571
##        B5    410    300    266    469    141
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.829729e-01             NA   6.812294e-01   6.847125e-01   6.712663e-01 
## AccuracyPValue  McnemarPValue 
##   1.601032e-39   0.000000e+00
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## [1] "in MFO.Classifier$predict"
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 184466      0      0      0      0
##        B2  52259      0      0      0      0
##        B3  24586      0      0      0      0
##        B4  11906      0      0      0      0
##        B5   1586      0      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.6712663             NA      0.6695064      0.6730227      0.6712663 
## AccuracyPValue  McnemarPValue 
##      0.5009025            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 123803  35402  16329   7862   1070
##        B2  35169   9822   4708   2274    286
##        B3  16479   4731   2205   1036    135
##        B4   7964   2307   1095    481     59
##        B5   1041    324    143     71      7
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.4960572             NA      0.4941860      0.4979284      0.6712663 
## AccuracyPValue  McnemarPValue 
##      1.0000000      0.6148447
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 184466      0      0      0      0
##        B2  52259      0      0      0      0
##        B3  24586      0      0      0      0
##        B4  11906      0      0      0      0
##        B5   1586      0      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.6712663             NA      0.6695064      0.6730227      0.6712663 
## AccuracyPValue  McnemarPValue 
##      0.5009025            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 164967  19499      0      0      0
##        B2  24001  28258      0      0      0
##        B3  10679  13907      0      0      0
##        B4   4020   7886      0      0      0
##        B5    410   1176      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.031401e-01             NA   7.014279e-01   7.048479e-01   6.712663e-01 
## AccuracyPValue  McnemarPValue 
##  3.041172e-282            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 165494  18972      0      0      0
##        B2  24418  27841      0      0      0
##        B3  10858  13728      0      0      0
##        B4   4097   7809      0      0      0
##        B5    418   1168      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.035404e-01             NA   7.018289e-01   7.052475e-01   6.712663e-01 
## AccuracyPValue  McnemarPValue 
##  2.207353e-289            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 171934  12532      0      0      0
##        B2  29612  22647      0      0      0
##        B3  13099  11487      0      0      0
##        B4   5037   6869      0      0      0
##        B5    523   1063      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.7080745             NA      0.7063706      0.7097740      0.6712663 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 172245  12221      0      0      0
##        B2  29908  22351      0      0      0
##        B3  13223  11363      0      0      0
##        B4   5096   6810      0      0      0
##        B5    530   1056      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.7081291             NA      0.7064253      0.7098285      0.6712663 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 171981  12159    140    186      0
##        B2  26534  25373    156    196      0
##        B3  12021  12119    286    160      0
##        B4   4652   6824     70    360      0
##        B5    483   1029     14     60      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.7205162             NA      0.7188342      0.7221934      0.6712663 
## AccuracyPValue  McnemarPValue 
##      0.0000000      0.0000000
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 165494  18972      0      0      0
##        B2  24418  27841      0      0      0
##        B3  10858  13728      0      0      0
##        B4   4097   7809      0      0      0
##        B5    418   1168      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.035404e-01             NA   7.018289e-01   7.052475e-01   6.712663e-01 
## AccuracyPValue  McnemarPValue 
##  2.207353e-289            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 171981  12159    140    186      0
##        B2  26534  25373    156    196      0
##        B3  12021  12119    286    160      0
##        B4   4652   6824     70    360      0
##        B5    483   1029     14     60      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.7205162             NA      0.7188342      0.7221934      0.6712663 
## AccuracyPValue  McnemarPValue 
##      0.0000000      0.0000000
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 173342  11124      0      0      0
##        B2  27769  24490      0      0      0
##        B3  12454  12124      8      0      0
##        B4   4772   7134      0      0      0
##        B5    497   1089      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.7199339             NA      0.7182509      0.7216123      0.6712663 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 184466      0      0      0      0
##        B2      0  52259      0      0      0
##        B3      0      0  24586      0      0
##        B4      0      0      0  11906      0
##        B5      0      0      0      0   1586
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      1.0000000             NA      0.9999866      1.0000000      0.6712663 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 114141   8610    124    103      0
##        B2  18409  16102    187    142      0
##        B3   8027   8146    118     99      0
##        B4   3099   4584     53    201      0
##        B5    351    657      4     45      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.126669e-01             NA   7.105887e-01   7.147384e-01   6.712700e-01 
## AccuracyPValue  McnemarPValue 
##  7.015238e-319   0.000000e+00
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 115108   7870      0      0      0
##        B2  19053  15787      0      0      0
##        B3   8254   8136      0      0      0
##        B4   3211   4726      0      0      0
##        B5    357    700      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.7144846             NA      0.7124102      0.7165521      0.6712700 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 114902   8076      0      0      0
##        B2  20130  14710      0      0      0
##        B3   8749   7641      0      0      0
##        B4   3409   4528      0      0      0
##        B5    382    675      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.074814e-01             NA   7.053922e-01   7.095639e-01   6.712700e-01 
## AccuracyPValue  McnemarPValue 
##  8.205962e-244            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 114141   8610    124    103      0
##        B2  18409  16102    187    142      0
##        B3   8027   8146    118     99      0
##        B4   3099   4584     53    201      0
##        B5    351    657      4     45      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.126669e-01             NA   7.105887e-01   7.147384e-01   6.712700e-01 
## AccuracyPValue  McnemarPValue 
##  7.015238e-319   0.000000e+00
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 111666   9606   1277    416     13
##        B2  18015  13793   2212    801     19
##        B3   8026   6727   1164    463     10
##        B4   3149   3541    715    512     20
##        B5    361    457    121    115      3
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.939771e-01             NA   6.918611e-01   6.960870e-01   6.712700e-01 
## AccuracyPValue  McnemarPValue 
##   1.592264e-96   0.000000e+00
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 110452  12526      0      0      0
##        B2  16322  18518      0      0      0
##        B3   7105   9285      0      0      0
##        B4   2740   5197      0      0      0
##        B5    299    758      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.039770e-01             NA   7.018807e-01   7.060669e-01   6.712700e-01 
## AccuracyPValue  McnemarPValue 
##  6.148674e-199            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## [1] "in Baseline.Classifier$predict"
## [1] "class(newdata):"
## [1] "matrix"
## [1] "head(newdata):"
##    bucket2008.fctrB2 bucket2008.fctrB3 bucket2008.fctrB4 bucket2008.fctrB5
## 3                  0                 0                 0                 0
## 5                  0                 0                 0                 0
## 6                  0                 0                 0                 0
## 8                  0                 0                 0                 0
## 9                  0                 0                 0                 0
## 10                 0                 0                 0                 0
##           .rnorm
## 3   0.7183313438
## 5   0.0008759006
## 6  -1.4428971189
## 8  -1.9680342619
## 9  -3.0026827087
## 10 -1.1723168041
## [1] "x_names: "
## [1] "bucket2008.fctrB2" "bucket2008.fctrB3" "bucket2008.fctrB4"
## [4] "bucket2008.fctrB5"
## [1] "x_vals: "
## [1] "B1" "B2" "B3" "B4" "B5"
## [1] "length(y):"
## [1] 183202
## [1] "head(y):"
## [1] B1 B1 B1 B1 B1 B1
## Levels: B1 B2 B3 B4 B5
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 110138   7787   3427   1452    174
##        B2  16000  10721   4629   2931    559
##        B3   7006   4629   2774   1621    360
##        B4   2688   1943   1415   1539    352
##        B5    293    191    160    309    104
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.838135e-01             NA   6.816786e-01   6.859425e-01   6.712700e-01 
## AccuracyPValue  McnemarPValue 
##   9.943570e-31   0.000000e+00
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 110452  12526      0      0      0
##        B2  16322  18518      0      0      0
##        B3   7105   9285      0      0      0
##        B4   2740   5197      0      0      0
##        B5    299    758      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.039770e-01             NA   7.018807e-01   7.060669e-01   6.712700e-01 
## AccuracyPValue  McnemarPValue 
##  6.148674e-199            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 114716   8262      0      0      0
##        B2  19896  14944      0      0      0
##        B3   8672   7718      0      0      0
##        B4   3366   4571      0      0      0
##        B5    379    678      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.077434e-01             NA   7.056548e-01   7.098254e-01   6.712700e-01 
## AccuracyPValue  McnemarPValue 
##  2.343725e-247            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 122978      0      0      0      0
##        B2  34840      0      0      0      0
##        B3  16390      0      0      0      0
##        B4   7937      0      0      0      0
##        B5   1057      0      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.6712700             NA      0.6691135      0.6734210      0.6712700 
## AccuracyPValue  McnemarPValue 
##      0.5011054            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 110138  12840      0      0      0
##        B2  16000  18840      0      0      0
##        B3   7006   9384      0      0      0
##        B4   2688   5249      0      0      0
##        B5    293    764      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.040207e-01             NA   7.019245e-01   7.061105e-01   6.712700e-01 
## AccuracyPValue  McnemarPValue 
##  1.813355e-199            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## [1] "in MFO.Classifier$predict"
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 122978      0      0      0      0
##        B2  34840      0      0      0      0
##        B3  16390      0      0      0      0
##        B4   7937      0      0      0      0
##        B5   1057      0      0      0      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.6712700             NA      0.6691135      0.6734210      0.6712700 
## AccuracyPValue  McnemarPValue 
##      0.5011054            NaN
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference    B1    B2    B3    B4    B5
##        B1 82872 23225 10833  5356   692
##        B2 23229  6624  3189  1594   204
##        B3 10992  3080  1504   735    79
##        B4  5323  1542   704   328    40
##        B5   674   219   112    45     7
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.4985481             NA      0.4962558      0.5008403      0.6712700 
## AccuracyPValue  McnemarPValue 
##      1.0000000      0.3234960
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
## [1] "Merging following data into glb_models_df:"
##                       model_id min.loss.error.fit min.loss.error.OOB
## 1  All.X.lser.no.cp.4015.rpart          0.7447408          0.7578902
## 2      All.X.lser.no.cp.opt.rf          0.7583760          0.7668584
## 3   All.X.lser.no.cp.opt.rpart          0.7861887          0.7875787
## 4  All.X.lser.ys.cp.4015.rpart          0.7447408          0.7578902
## 5      All.X.lser.ys.cp.opt.rf          0.0000000          0.7655702
## 6   All.X.lser.ys.cp.opt.rpart          0.7455013          0.7441403
## 7    Baseline.mybaseln_classfr          0.7402976          0.7386055
## 8    Interact.High.cor.y.rpart          0.7455013          0.7441403
## 9              Low.cor.X.rpart          0.7837833          0.7846967
## 10        Max.cor.Y.cv.0.rpart          1.0443336          1.0443008
## 11        Max.cor.Y.cv.G.rpart          0.7424628          0.7406251
## 12           MFO.myMFO_classfr          1.0443336          1.0443008
## 13     Random.myrandom_classfr          1.1765119          1.1735298
##                       model_id min.loss.error.fit min.loss.error.OOB
## 9  All.X.lser.no.cp.4015.rpart                 NA                 NA
## 12     All.X.lser.no.cp.opt.rf                 NA                 NA
## 8   All.X.lser.no.cp.opt.rpart                 NA                 NA
## 11 All.X.lser.ys.cp.4015.rpart          0.7582887          0.7578902
## 13     All.X.lser.ys.cp.opt.rf          0.0000000          0.7655102
## 10  All.X.lser.ys.cp.opt.rpart          0.7784778          0.7441403
## 1    Baseline.mybaseln_classfr                 NA                 NA
## 6    Interact.High.cor.y.rpart                 NA                 NA
## 7              Low.cor.X.rpart                 NA                 NA
## 4         Max.cor.Y.cv.0.rpart                 NA                 NA
## 5         Max.cor.Y.cv.G.rpart                 NA                 NA
## 2            MFO.myMFO_classfr                 NA                 NA
## 3      Random.myrandom_classfr                 NA                 NA
##                       model_id                  model_id.1
## 1    Baseline.mybaseln_classfr   Baseline.mybaseln_classfr
## 2            MFO.myMFO_classfr           MFO.myMFO_classfr
## 3      Random.myrandom_classfr     Random.myrandom_classfr
## 4         Max.cor.Y.cv.0.rpart        Max.cor.Y.cv.0.rpart
## 5         Max.cor.Y.cv.G.rpart        Max.cor.Y.cv.G.rpart
## 6    Interact.High.cor.y.rpart   Interact.High.cor.y.rpart
## 7              Low.cor.X.rpart             Low.cor.X.rpart
## 8   All.X.lser.no.cp.opt.rpart  All.X.lser.no.cp.opt.rpart
## 9  All.X.lser.no.cp.4015.rpart All.X.lser.no.cp.4015.rpart
## 10  All.X.lser.ys.cp.opt.rpart  All.X.lser.ys.cp.opt.rpart
## 11 All.X.lser.ys.cp.4015.rpart All.X.lser.ys.cp.4015.rpart
## 12     All.X.lser.no.cp.opt.rf     All.X.lser.no.cp.opt.rf
## 13     All.X.lser.ys.cp.opt.rf     All.X.lser.ys.cp.opt.rf
##        model_method
## 1  mybaseln_classfr
## 2     myMFO_classfr
## 3  myrandom_classfr
## 4             rpart
## 5             rpart
## 6             rpart
## 7             rpart
## 8             rpart
## 9             rpart
## 10            rpart
## 11            rpart
## 12               rf
## 13               rf
##                                                                                                                                                      feats
## 1                                                                                                                                  bucket2008.fctr, .rnorm
## 2                                                                                                                                                   .rnorm
## 3                                                                                                                                                   .rnorm
## 4                                                                                                                                               bucket2008
## 5                                                                                                                                               bucket2008
## 6                                                                                                                            bucket2008, reimbursement2008
## 7                             bucket2008, ihd, diabetes, kidney, heart.failure, copd, depression, alzheimers, arthritis, cancer, osteoporosis, stroke, age
## 8          age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008
## 9          age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008
## 10         age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008
## 11         age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008
## 12 age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008, .rnorm
## 13 age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008, .rnorm
##    max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1                0                      0.626                 0.006
## 2                0                      0.515                 0.038
## 3                0                      0.375                 0.035
## 4                0                      4.811                 3.430
## 5                3                     20.801                 3.333
## 6                3                     31.301                 5.050
## 7                3                    101.182                13.937
## 8                3                    107.610                15.544
## 9                1                     73.967                15.630
## 10               3                    108.644                15.505
## 11               1                     75.838                16.151
## 12               3                   3479.970               449.343
## 13               3                   4381.707              1409.455
##    max.Accuracy.fit max.AccuracyLower.fit max.AccuracyUpper.fit
## 1         0.6829729             0.6812294             0.6847125
## 2         0.6712663             0.6695064             0.6730227
## 3         0.4967267             0.4948556             0.4985980
## 4         0.6712663             0.6695064             0.6730227
## 5         0.7031401             0.7014279             0.7048479
## 6         0.7029800             0.7018289             0.7052475
## 7         0.7086058             0.7063706             0.7097740
## 8         0.7086604             0.7064253             0.7098285
## 9         0.7117062             0.7188342             0.7221934
## 10        0.7035404             0.7018289             0.7052475
## 11        0.7205162             0.7188342             0.7221934
## 12        0.7139405             0.7182509             0.7216123
## 13        1.0000000             0.9999866             1.0000000
##    max.Kappa.fit max.Accuracy.OOB max.AccuracyLower.OOB
## 1             NA        0.6838135             0.6816786
## 2             NA        0.6712700             0.6691135
## 3             NA        0.4948800             0.4925879
## 4             NA        0.6712700             0.6691135
## 5      0.3440289        0.7040207             0.7019245
## 6      0.3368565        0.7039770             0.7018807
## 7      0.3161651        0.7077434             0.7056548
## 8      0.3140169        0.7074814             0.7053922
## 9      0.3328722        0.7126669             0.7105887
## 10            NA        0.7039770             0.7018807
## 11            NA        0.7126669             0.7105887
## 12     0.3281909        0.7144900             0.7124157
## 13            NA        0.6939553             0.6918392
##    max.AccuracyUpper.OOB max.Kappa.OOB min.SSE.fit max.AccuracySD.fit
## 1              0.6859425            NA           0                 NA
## 2              0.6734210            NA           0                 NA
## 3              0.4971722            NA           0                 NA
## 4              0.6734210            NA           0                 NA
## 5              0.7061105            NA           0       0.0016657885
## 6              0.7060669            NA           0       0.0016900317
## 7              0.7098254            NA           0       0.0009742811
## 8              0.7095639            NA           0       0.0010140545
## 9              0.7147384            NA           0       0.0021496212
## 10             0.7060669            NA           0                 NA
## 11             0.7147384            NA           0                 NA
## 12             0.7165575            NA           0                 NA
## 13             0.6960652            NA           0                 NA
##    max.KappaSD.fit min.loss.errorSD.fit min.Accuracy.fit min.Kappa.fit
## 1               NA                   NA               NA            NA
## 2               NA                   NA               NA            NA
## 3               NA                   NA               NA            NA
## 4               NA                   NA               NA            NA
## 5      0.004508795                   NA               NA            NA
## 6      0.006361514                   NA               NA            NA
## 7      0.004868262                   NA               NA            NA
## 8      0.004809558                   NA               NA            NA
## 9      0.007338010                   NA               NA            NA
## 10              NA          0.016425281               NA            NA
## 11              NA          0.005993538               NA            NA
## 12              NA                   NA               NA            NA
## 13              NA                   NA        0.6943265     0.3128527
##    min.loss.error.fit min.loss.error.OOB
## 1           0.7402976          0.7386055
## 2           1.0443336          1.0443008
## 3           1.1765119          1.1735298
## 4           1.0443336          1.0443008
## 5           0.7424628          0.7406251
## 6           0.7455013          0.7441403
## 7           0.7837833          0.7846967
## 8           0.7861887          0.7875787
## 9           0.7447408          0.7578902
## 10          0.7455013          0.7441403
## 11          0.7447408          0.7578902
## 12          0.7583760          0.7668584
## 13          0.0000000          0.7655702
##  [1] "model_id"                   "model_id.1"                
##  [3] "model_method"               "feats"                     
##  [5] "max.nTuningRuns"            "min.elapsedtime.everything"
##  [7] "min.elapsedtime.final"      "max.Accuracy.fit"          
##  [9] "max.AccuracyLower.fit"      "max.AccuracyUpper.fit"     
## [11] "max.Kappa.fit"              "max.Accuracy.OOB"          
## [13] "max.AccuracyLower.OOB"      "max.AccuracyUpper.OOB"     
## [15] "max.Kappa.OOB"              "min.SSE.fit"               
## [17] "max.AccuracySD.fit"         "max.KappaSD.fit"           
## [19] "min.loss.errorSD.fit"       "min.Accuracy.fit"          
## [21] "min.Kappa.fit"              "min.loss.error.fit"        
## [23] "min.loss.error.OOB"        
##                       model_id     model_method
## 1    Baseline.mybaseln_classfr mybaseln_classfr
## 2            MFO.myMFO_classfr    myMFO_classfr
## 3      Random.myrandom_classfr myrandom_classfr
## 4         Max.cor.Y.cv.0.rpart            rpart
## 5         Max.cor.Y.cv.G.rpart            rpart
## 6    Interact.High.cor.y.rpart            rpart
## 7              Low.cor.X.rpart            rpart
## 8   All.X.lser.no.cp.opt.rpart            rpart
## 9  All.X.lser.no.cp.4015.rpart            rpart
## 10  All.X.lser.ys.cp.opt.rpart            rpart
## 11 All.X.lser.ys.cp.4015.rpart            rpart
## 12     All.X.lser.no.cp.opt.rf               rf
## 13     All.X.lser.ys.cp.opt.rf               rf
##                                                                                                                                                      feats
## 1                                                                                                                                  bucket2008.fctr, .rnorm
## 2                                                                                                                                                   .rnorm
## 3                                                                                                                                                   .rnorm
## 4                                                                                                                                               bucket2008
## 5                                                                                                                                               bucket2008
## 6                                                                                                                            bucket2008, reimbursement2008
## 7                             bucket2008, ihd, diabetes, kidney, heart.failure, copd, depression, alzheimers, arthritis, cancer, osteoporosis, stroke, age
## 8          age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008
## 9          age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008
## 10         age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008
## 11         age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008
## 12 age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008, .rnorm
## 13 age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008, .rnorm
##    max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1                0                      0.626                 0.006
## 2                0                      0.515                 0.038
## 3                0                      0.375                 0.035
## 4                0                      4.811                 3.430
## 5                3                     20.801                 3.333
## 6                3                     31.301                 5.050
## 7                3                    101.182                13.937
## 8                3                    107.610                15.544
## 9                1                     73.967                15.630
## 10               3                    108.644                15.505
## 11               1                     75.838                16.151
## 12               3                   3479.970               449.343
## 13               3                   4381.707              1409.455
##    max.Accuracy.fit max.AccuracyLower.fit max.AccuracyUpper.fit
## 1         0.6829729             0.6812294             0.6847125
## 2         0.6712663             0.6695064             0.6730227
## 3         0.4967267             0.4948556             0.4985980
## 4         0.6712663             0.6695064             0.6730227
## 5         0.7031401             0.7014279             0.7048479
## 6         0.7029800             0.7018289             0.7052475
## 7         0.7086058             0.7063706             0.7097740
## 8         0.7086604             0.7064253             0.7098285
## 9         0.7117062             0.7188342             0.7221934
## 10        0.7035404             0.7018289             0.7052475
## 11        0.7205162             0.7188342             0.7221934
## 12        0.7139405             0.7182509             0.7216123
## 13        1.0000000             0.9999866             1.0000000
##    max.Kappa.fit max.Accuracy.OOB max.AccuracyLower.OOB
## 1             NA        0.6838135             0.6816786
## 2             NA        0.6712700             0.6691135
## 3             NA        0.4948800             0.4925879
## 4             NA        0.6712700             0.6691135
## 5      0.3440289        0.7040207             0.7019245
## 6      0.3368565        0.7039770             0.7018807
## 7      0.3161651        0.7077434             0.7056548
## 8      0.3140169        0.7074814             0.7053922
## 9      0.3328722        0.7126669             0.7105887
## 10            NA        0.7039770             0.7018807
## 11            NA        0.7126669             0.7105887
## 12     0.3281909        0.7144900             0.7124157
## 13            NA        0.6939553             0.6918392
##    max.AccuracyUpper.OOB max.Kappa.OOB min.SSE.fit max.AccuracySD.fit
## 1              0.6859425            NA           0                 NA
## 2              0.6734210            NA           0                 NA
## 3              0.4971722            NA           0                 NA
## 4              0.6734210            NA           0                 NA
## 5              0.7061105            NA           0       0.0016657885
## 6              0.7060669            NA           0       0.0016900317
## 7              0.7098254            NA           0       0.0009742811
## 8              0.7095639            NA           0       0.0010140545
## 9              0.7147384            NA           0       0.0021496212
## 10             0.7060669            NA           0                 NA
## 11             0.7147384            NA           0                 NA
## 12             0.7165575            NA           0                 NA
## 13             0.6960652            NA           0                 NA
##    max.KappaSD.fit min.loss.errorSD.fit min.Accuracy.fit min.Kappa.fit
## 1               NA                   NA               NA            NA
## 2               NA                   NA               NA            NA
## 3               NA                   NA               NA            NA
## 4               NA                   NA               NA            NA
## 5      0.004508795                   NA               NA            NA
## 6      0.006361514                   NA               NA            NA
## 7      0.004868262                   NA               NA            NA
## 8      0.004809558                   NA               NA            NA
## 9      0.007338010                   NA               NA            NA
## 10              NA          0.016425281               NA            NA
## 11              NA          0.005993538               NA            NA
## 12              NA                   NA               NA            NA
## 13              NA                   NA        0.6943265     0.3128527
##    min.loss.error.fit min.loss.error.OOB
## 1           0.7402976          0.7386055
## 2           1.0443336          1.0443008
## 3           1.1765119          1.1735298
## 4           1.0443336          1.0443008
## 5           0.7424628          0.7406251
## 6           0.7455013          0.7441403
## 7           0.7837833          0.7846967
## 8           0.7861887          0.7875787
## 9           0.7447408          0.7578902
## 10          0.7455013          0.7441403
## 11          0.7447408          0.7578902
## 12          0.7583760          0.7668584
## 13          0.0000000          0.7655702
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
    plt_models_df[, sub("min.", "inv.", var)] <- 
        #ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
        1.0 / plt_models_df[, var]
    plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)
##                       model_id     model_method
## 1    Baseline.mybaseln_classfr mybaseln_classfr
## 2            MFO.myMFO_classfr    myMFO_classfr
## 3      Random.myrandom_classfr myrandom_classfr
## 4         Max.cor.Y.cv.0.rpart            rpart
## 5         Max.cor.Y.cv.G.rpart            rpart
## 6    Interact.High.cor.y.rpart            rpart
## 7              Low.cor.X.rpart            rpart
## 8   All.X.lser.no.cp.opt.rpart            rpart
## 9  All.X.lser.no.cp.4015.rpart            rpart
## 10  All.X.lser.ys.cp.opt.rpart            rpart
## 11 All.X.lser.ys.cp.4015.rpart            rpart
## 12     All.X.lser.no.cp.opt.rf               rf
## 13     All.X.lser.ys.cp.opt.rf               rf
##                                                                                                                                                      feats
## 1                                                                                                                                  bucket2008.fctr, .rnorm
## 2                                                                                                                                                   .rnorm
## 3                                                                                                                                                   .rnorm
## 4                                                                                                                                               bucket2008
## 5                                                                                                                                               bucket2008
## 6                                                                                                                            bucket2008, reimbursement2008
## 7                             bucket2008, ihd, diabetes, kidney, heart.failure, copd, depression, alzheimers, arthritis, cancer, osteoporosis, stroke, age
## 8          age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008
## 9          age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008
## 10         age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008
## 11         age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008
## 12 age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008, .rnorm
## 13 age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008, .rnorm
##    max.nTuningRuns max.Accuracy.fit max.Kappa.fit max.Accuracy.OOB
## 1                0        0.6829729            NA        0.6838135
## 2                0        0.6712663            NA        0.6712700
## 3                0        0.4967267            NA        0.4948800
## 4                0        0.6712663            NA        0.6712700
## 5                3        0.7031401     0.3440289        0.7040207
## 6                3        0.7029800     0.3368565        0.7039770
## 7                3        0.7086058     0.3161651        0.7077434
## 8                3        0.7086604     0.3140169        0.7074814
## 9                1        0.7117062     0.3328722        0.7126669
## 10               3        0.7035404            NA        0.7039770
## 11               1        0.7205162            NA        0.7126669
## 12               3        0.7139405     0.3281909        0.7144900
## 13               3        1.0000000            NA        0.6939553
##    max.Kappa.OOB inv.elapsedtime.everything inv.elapsedtime.final
## 1             NA               1.5974440895          1.666667e+02
## 2             NA               1.9417475728          2.631579e+01
## 3             NA               2.6666666667          2.857143e+01
## 4             NA               0.2078569944          2.915452e-01
## 5             NA               0.0480746118          3.000300e-01
## 6             NA               0.0319478611          1.980198e-01
## 7             NA               0.0098831808          7.175145e-02
## 8             NA               0.0092928167          6.433350e-02
## 9             NA               0.0135195425          6.397953e-02
## 10            NA               0.0092043739          6.449532e-02
## 11            NA               0.0131860017          6.191567e-02
## 12            NA               0.0002873588          2.225471e-03
## 13            NA               0.0002282216          7.094941e-04
##    inv.SSE.fit inv.Accuracy.fit inv.Kappa.fit inv.loss.error.fit
## 1          Inf               NA            NA          1.3508081
## 2          Inf               NA            NA          0.9575485
## 3          Inf               NA            NA          0.8499702
## 4          Inf               NA            NA          0.9575485
## 5          Inf               NA            NA          1.3468689
## 6          Inf               NA            NA          1.3413792
## 7          Inf               NA            NA          1.2758629
## 8          Inf               NA            NA          1.2719593
## 9          Inf               NA            NA          1.3427491
## 10         Inf               NA            NA          1.3413792
## 11         Inf               NA            NA          1.3427491
## 12         Inf               NA            NA          1.3186071
## 13         Inf         1.440245      3.196392                Inf
##    inv.loss.error.OOB
## 1           1.3539028
## 2           0.9575785
## 3           0.8521301
## 4           0.9575785
## 5           1.3502108
## 6           1.3438325
## 7           1.2743778
## 8           1.2697143
## 9           1.3194523
## 10          1.3438325
## 11          1.3194523
## 12          1.3040216
## 13          1.3062159
print(myplot_radar(radar_inp_df=plt_models_df))
## Warning in myplot_radar(radar_inp_df = plt_models_df): Not plotting
## columns with all NAs: max.Kappa.OOB
## Warning in myplot_radar(radar_inp_df = plt_models_df): Not plotting
## columns with all Infs: inv.SSE.fit,inv.loss.error.fit
## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors
## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually. if you must have them.
## Warning: Removed 83 rows containing missing values (geom_point).
## Warning: Removed 31 rows containing missing values (geom_text).
## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors
## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually. if you must have them.

print(myplot_radar(radar_inp_df=subset(plt_models_df, 
        !(model_id %in% grep("random|MFO", plt_models_df$model_id, value=TRUE)))))
## Warning in myplot_radar(radar_inp_df = subset(plt_models_df, !(model_id
## %in% : Not plotting columns with all NAs: max.Kappa.OOB
## Warning in myplot_radar(radar_inp_df = subset(plt_models_df, !(model_id
## %in% : Not plotting columns with all Infs: inv.SSE.fit,inv.loss.error.fit
## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors
## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 11. Consider specifying shapes manually. if you must have them.
## Warning: Removed 63 rows containing missing values (geom_point).
## Warning: Removed 25 rows containing missing values (geom_text).
## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors
## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 11. Consider specifying shapes manually. if you must have them.

# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df, 
                max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
                min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
    # Does CI alredy exist ?
    var_components <- unlist(strsplit(var, "SD"))
    varActul <- paste0(var_components[1],          var_components[2])
    varUpper <- paste0(var_components[1], "Upper", var_components[2])
    varLower <- paste0(var_components[1], "Lower", var_components[2])
    if (varUpper %in% names(glb_models_df)) {
        warning(varUpper, " already exists in glb_models_df")
        # Assuming Lower also exists
        next
    }    
    print(sprintf("var:%s", var))
    # CI is dependent on sample size in t distribution; df=n-1
    glb_models_df[, varUpper] <- glb_models_df[, varActul] + 
        glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
    glb_models_df[, varLower] <- glb_models_df[, varActul] - 
        glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}
## Warning: max.AccuracyUpper.fit already exists in glb_models_df
## [1] "var:max.KappaSD.fit"
## [1] "var:min.loss.errorSD.fit"
# Plot metrics with CI
plt_models_df <- glb_models_df[, "model_id", FALSE]
pltCI_models_df <- glb_models_df[, "model_id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
    var_components <- unlist(strsplit(var, "Upper"))
    col_name <- unlist(paste(var_components, collapse=""))
    plt_models_df[, col_name] <- glb_models_df[, col_name]
    for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
        pltCI_models_df[, name] <- glb_models_df[, name]
}

build_statsCI_data <- function(plt_models_df) {
    mltd_models_df <- melt(plt_models_df, id.vars="model_id")
    mltd_models_df$data <- sapply(1:nrow(mltd_models_df), 
        function(row_ix) tail(unlist(strsplit(as.character(
            mltd_models_df[row_ix, "variable"]), "[.]")), 1))
    mltd_models_df$label <- sapply(1:nrow(mltd_models_df), 
        function(row_ix) head(unlist(strsplit(as.character(
            mltd_models_df[row_ix, "variable"]), paste0(".", mltd_models_df[row_ix, "data"]))), 1))
    #print(mltd_models_df)
    
    return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)

mltdCI_models_df <- melt(pltCI_models_df, id.vars="model_id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
    for (type in c("Upper", "Lower")) {
        if (length(var_components <- unlist(strsplit(
                as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
            #print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
            mltdCI_models_df[row_ix, "label"] <- var_components[1]
            mltdCI_models_df[row_ix, "data"] <- unlist(strsplit(var_components[2], "[.]"))[2]
            mltdCI_models_df[row_ix, "type"] <- type
            break
        }
    }    
}
#print(mltdCI_models_df)
# castCI_models_df <- dcast(mltdCI_models_df, value ~ type, fun.aggregate=sum)
# print(castCI_models_df)
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable), 
                            timevar="type", 
        idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")), 
                            direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)

# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
    for (type in unique(mltd_models_df$data)) {
        var_type <- paste0(var, ".", type)
        # if this data is already present, next
        if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data, sep=".")))
            next
        #print(sprintf("var_type:%s", var_type))
        goback_vars <- c(goback_vars, var_type)
    }
}

mltd_goback_df <- build_statsCI_data(glb_models_df[, c("model_id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)

mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("model_id", "model_method")], all.x=TRUE)

# print(myplot_bar(mltd_models_df, "model_id", "value", colorcol_name="data") + 
#         geom_errorbar(data=mrgdCI_models_df, 
#             mapping=aes(x=model_id, ymax=value.Upper, ymin=value.Lower), width=0.5) + 
#           facet_grid(label ~ data, scales="free") + 
#           theme(axis.text.x = element_text(angle = 45,vjust = 1)))
# mltd_models_df <- orderBy(~ value +variable +data +label + model_method + model_id, 
#                           mltd_models_df)
print(myplot_bar(mltd_models_df, "model_id", "value", colorcol_name="model_method") + 
        geom_errorbar(data=mrgdCI_models_df, 
            mapping=aes(x=model_id, ymax=value.Upper, ymin=value.Lower), width=0.5) + 
          facet_grid(label ~ data, scales="free") + 
          theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))
## Warning: Removed 7 rows containing missing values (position_stack).
## Warning: Removed 13 rows containing missing values (position_stack).
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning: position_stack requires constant width: output may be incorrect

if (glb_is_regression) {
    print(orderBy(~ -R.sq.OOB -Adj.R.sq.fit, glb_models_df))
    stop("glb_sel_mdl not selected")
    print(myplot_scatter(plot_models_df, "Adj.R.sq.fit", "R.sq.OOB") + 
          geom_text(aes(label=feats.label), data=plot_models_df, color="NavyBlue", 
                    size=3.5, angle=45))
}    

if (glb_is_classification) {
    print(tmp_models_df <- orderBy(glb_model_sel_frmla, glb_models_df))    
    print("Metrics used for model selection:"); print(glb_model_sel_frmla)
    print(sprintf("Best model id: %s", tmp_models_df[1, "model_id"]))
    
    if (is.null(glb_sel_mdl_id)) 
        { glb_sel_mdl_id <- tmp_models_df[1, "model_id"] } else 
        print(sprintf("User specified selection: %s", glb_sel_mdl_id))   
    
    myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])
}        
##                       model_id     model_method
## 1    Baseline.mybaseln_classfr mybaseln_classfr
## 5         Max.cor.Y.cv.G.rpart            rpart
## 6    Interact.High.cor.y.rpart            rpart
## 10  All.X.lser.ys.cp.opt.rpart            rpart
## 9  All.X.lser.no.cp.4015.rpart            rpart
## 11 All.X.lser.ys.cp.4015.rpart            rpart
## 13     All.X.lser.ys.cp.opt.rf               rf
## 12     All.X.lser.no.cp.opt.rf               rf
## 7              Low.cor.X.rpart            rpart
## 8   All.X.lser.no.cp.opt.rpart            rpart
## 2            MFO.myMFO_classfr    myMFO_classfr
## 4         Max.cor.Y.cv.0.rpart            rpart
## 3      Random.myrandom_classfr myrandom_classfr
##                                                                                                                                                      feats
## 1                                                                                                                                  bucket2008.fctr, .rnorm
## 5                                                                                                                                               bucket2008
## 6                                                                                                                            bucket2008, reimbursement2008
## 10         age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008
## 9          age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008
## 11         age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008
## 13 age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008, .rnorm
## 12 age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008, .rnorm
## 7                             bucket2008, ihd, diabetes, kidney, heart.failure, copd, depression, alzheimers, arthritis, cancer, osteoporosis, stroke, age
## 8          age, alzheimers, arthritis, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, reimbursement2008, bucket2008
## 2                                                                                                                                                   .rnorm
## 4                                                                                                                                               bucket2008
## 3                                                                                                                                                   .rnorm
##    max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1                0                      0.626                 0.006
## 5                3                     20.801                 3.333
## 6                3                     31.301                 5.050
## 10               3                    108.644                15.505
## 9                1                     73.967                15.630
## 11               1                     75.838                16.151
## 13               3                   4381.707              1409.455
## 12               3                   3479.970               449.343
## 7                3                    101.182                13.937
## 8                3                    107.610                15.544
## 2                0                      0.515                 0.038
## 4                0                      4.811                 3.430
## 3                0                      0.375                 0.035
##    max.Accuracy.fit max.AccuracyLower.fit max.AccuracyUpper.fit
## 1         0.6829729             0.6812294             0.6847125
## 5         0.7031401             0.7014279             0.7048479
## 6         0.7029800             0.7018289             0.7052475
## 10        0.7035404             0.7018289             0.7052475
## 9         0.7117062             0.7188342             0.7221934
## 11        0.7205162             0.7188342             0.7221934
## 13        1.0000000             0.9999866             1.0000000
## 12        0.7139405             0.7182509             0.7216123
## 7         0.7086058             0.7063706             0.7097740
## 8         0.7086604             0.7064253             0.7098285
## 2         0.6712663             0.6695064             0.6730227
## 4         0.6712663             0.6695064             0.6730227
## 3         0.4967267             0.4948556             0.4985980
##    max.Kappa.fit max.Accuracy.OOB max.AccuracyLower.OOB
## 1             NA        0.6838135             0.6816786
## 5      0.3440289        0.7040207             0.7019245
## 6      0.3368565        0.7039770             0.7018807
## 10            NA        0.7039770             0.7018807
## 9      0.3328722        0.7126669             0.7105887
## 11            NA        0.7126669             0.7105887
## 13            NA        0.6939553             0.6918392
## 12     0.3281909        0.7144900             0.7124157
## 7      0.3161651        0.7077434             0.7056548
## 8      0.3140169        0.7074814             0.7053922
## 2             NA        0.6712700             0.6691135
## 4             NA        0.6712700             0.6691135
## 3             NA        0.4948800             0.4925879
##    max.AccuracyUpper.OOB max.Kappa.OOB min.SSE.fit max.AccuracySD.fit
## 1              0.6859425            NA           0                 NA
## 5              0.7061105            NA           0       0.0016657885
## 6              0.7060669            NA           0       0.0016900317
## 10             0.7060669            NA           0                 NA
## 9              0.7147384            NA           0       0.0021496212
## 11             0.7147384            NA           0                 NA
## 13             0.6960652            NA           0                 NA
## 12             0.7165575            NA           0                 NA
## 7              0.7098254            NA           0       0.0009742811
## 8              0.7095639            NA           0       0.0010140545
## 2              0.6734210            NA           0                 NA
## 4              0.6734210            NA           0                 NA
## 3              0.4971722            NA           0                 NA
##    max.KappaSD.fit min.loss.errorSD.fit min.Accuracy.fit min.Kappa.fit
## 1               NA                   NA               NA            NA
## 5      0.004508795                   NA               NA            NA
## 6      0.006361514                   NA               NA            NA
## 10              NA          0.016425281               NA            NA
## 9      0.007338010                   NA               NA            NA
## 11              NA          0.005993538               NA            NA
## 13              NA                   NA        0.6943265     0.3128527
## 12              NA                   NA               NA            NA
## 7      0.004868262                   NA               NA            NA
## 8      0.004809558                   NA               NA            NA
## 2               NA                   NA               NA            NA
## 4               NA                   NA               NA            NA
## 3               NA                   NA               NA            NA
##    min.loss.error.fit min.loss.error.OOB max.df min.sd2ci.scaler
## 1           0.7402976          0.7386055     NA               NA
## 5           0.7424628          0.7406251      2         4.302653
## 6           0.7455013          0.7441403      2         4.302653
## 10          0.7455013          0.7441403      2         4.302653
## 9           0.7447408          0.7578902     NA               NA
## 11          0.7447408          0.7578902     NA               NA
## 13          0.0000000          0.7655702      2         4.302653
## 12          0.7583760          0.7668584      2         4.302653
## 7           0.7837833          0.7846967      2         4.302653
## 8           0.7861887          0.7875787      2         4.302653
## 2           1.0443336          1.0443008     NA               NA
## 4           1.0443336          1.0443008     NA               NA
## 3           1.1765119          1.1735298     NA               NA
##    max.KappaUpper.fit max.KappaLower.fit min.loss.errorUpper.fit
## 1                  NA                 NA                      NA
## 5           0.3634286          0.3246291                      NA
## 6           0.3642279          0.3094851                      NA
## 10                 NA                 NA               0.8161736
## 9                  NA                 NA                      NA
## 11                 NA                 NA                      NA
## 13                 NA                 NA                      NA
## 12                 NA                 NA                      NA
## 7           0.3371115          0.2952186                      NA
## 8           0.3347108          0.2933231                      NA
## 2                  NA                 NA                      NA
## 4                  NA                 NA                      NA
## 3                  NA                 NA                      NA
##    min.loss.errorLower.fit
## 1                       NA
## 5                       NA
## 6                       NA
## 10                0.674829
## 9                       NA
## 11                      NA
## 13                      NA
## 12                      NA
## 7                       NA
## 8                       NA
## 2                       NA
## 4                       NA
## 3                       NA
## [1] "Metrics used for model selection:"
## ~+min.loss.error.OOB - max.Accuracy.OOB - max.Kappa.OOB
## [1] "Best model id: Baseline.mybaseln_classfr"
## [1] "User specified selection: All.X.lser.ys.cp.4015.rpart"
## Warning: labs do not fit even at cex 0.15, there may be some overplotting

## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7, 
##     cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, 
##     surrogatestyle = 0, maxdepth = 30, xval = 0))
##   n= 274803 
## 
##              CP nsplit rel error
## 1  4.908841e-02      0 1.0000000
## 2  1.395884e-02      2 0.9018232
## 3  4.350377e-03      3 0.8878643
## 4  3.597640e-03      4 0.8835140
## 5  9.879673e-04      5 0.8799163
## 6  7.859460e-04     10 0.8748685
## 7  6.789392e-04     11 0.8740826
## 8  4.649258e-04     14 0.8720458
## 9  4.372516e-04     15 0.8715809
## 10 2.988809e-04     19 0.8698319
## 11 2.767415e-04     20 0.8695330
## 12 2.435326e-04     21 0.8692562
## 13 2.036818e-04     22 0.8690127
## 14 1.881842e-04     28 0.8677729
## 15 1.826494e-04     29 0.8675847
## 16 1.605101e-04     31 0.8672194
## 17 1.439056e-04     33 0.8668984
## 18 1.411382e-04     37 0.8663228
## 19 1.217663e-04     42 0.8655922
## 20 1.162314e-04     45 0.8652269
## 21 1.129105e-04     47 0.8649944
## 22 1.051618e-04     52 0.8644299
## 23 9.962695e-05     57 0.8638764
## 24 9.409212e-05     68 0.8627473
## 25 8.855729e-05     74 0.8621827
## 26 8.302246e-05     83 0.8613746
## 27 7.748763e-05     85 0.8612086
## 28 7.379774e-05     97 0.8602455
## 29 7.195280e-05    101 0.8599134
## 30 6.918538e-05    114 0.8588729
## 31 6.641797e-05    122 0.8583194
## 32 6.272808e-05    154 0.8560612
## 33 6.167383e-05    158 0.8557955
## 34 6.088314e-05    166 0.8552752
## 35 5.811572e-05    179 0.8544340
## 36 5.534831e-05    183 0.8542015
## 37 5.313437e-05    228 0.8516001
## 38 5.258089e-05    233 0.8513344
## 39 5.165842e-05    237 0.8511241
## 40 5.000000e-05    254 0.8501832
## 
## Variable importance
## reimbursement2008        bucket2008          diabetes               ihd 
##                31                20                13                13 
##     heart.failure            kidney         arthritis 
##                11                 9                 1 
## 
## Node number 1: 274803 observations,    complexity param=0.04908841
##   predicted class=B1  expected loss=0.3287337  P(node) =1
##     class counts: 184466 52259 24586 11906  1586
##    probabilities: 0.671 0.190 0.089 0.043 0.006 
##   left son=2 (165987 obs) right son=3 (108816 obs)
##   Primary splits:
##       reimbursement2008 < 1565   to the left,  improve=24395.14, (0 missing)
##       bucket2008        < 1.5    to the left,  improve=20624.70, (0 missing)
##       ihd               < 0.5    to the left,  improve=16291.74, (0 missing)
##       diabetes          < 0.5    to the left,  improve=16041.26, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=12498.16, (0 missing)
##   Surrogate splits:
##       bucket2008    < 1.5    to the left,  agree=0.861, adj=0.650, (0 split)
##       ihd           < 0.5    to the left,  agree=0.792, adj=0.474, (0 split)
##       diabetes      < 0.5    to the left,  agree=0.785, adj=0.456, (0 split)
##       heart.failure < 0.5    to the left,  agree=0.762, adj=0.399, (0 split)
##       kidney        < 0.5    to the left,  agree=0.731, adj=0.321, (0 split)
## 
## Node number 2: 165987 observations
##   predicted class=B1  expected loss=0.1261424  P(node) =0.6040218
##     class counts: 145049 12284  6102  2315   237
##    probabilities: 0.874 0.074 0.037 0.014 0.001 
## 
## Node number 3: 108816 observations,    complexity param=0.04908841
##   predicted class=B2  expected loss=0.6326367  P(node) =0.3959782
##     class counts: 39417 39975 18484  9591  1349
##    probabilities: 0.362 0.367 0.170 0.088 0.012 
##   left son=6 (39298 obs) right son=7 (69518 obs)
##   Primary splits:
##       reimbursement2008 < 3065   to the left,  improve=2010.3080, (0 missing)
##       bucket2008        < 1.5    to the left,  improve=1980.9770, (0 missing)
##       kidney            < 0.5    to the left,  improve=1416.9220, (0 missing)
##       diabetes          < 0.5    to the left,  improve=1236.1460, (0 missing)
##       heart.failure     < 0.5    to the left,  improve= 976.9427, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the left,  agree=0.989, adj=0.969, (0 split)
##       ihd        < 0.5    to the left,  agree=0.659, adj=0.056, (0 split)
##       diabetes   < 0.5    to the left,  agree=0.641, adj=0.006, (0 split)
## 
## Node number 6: 39298 observations,    complexity param=0.0006789392
##   predicted class=B1  expected loss=0.4797445  P(node) =0.1430043
##     class counts: 20445 12134  4756  1782   181
##    probabilities: 0.520 0.309 0.121 0.045 0.005 
##   left son=12 (20077 obs) right son=13 (19221 obs)
##   Primary splits:
##       reimbursement2008 < 2175   to the left,  improve=192.7592, (0 missing)
##       diabetes          < 0.5    to the left,  improve=155.3521, (0 missing)
##       ihd               < 0.5    to the left,  improve=114.8541, (0 missing)
##       arthritis         < 0.5    to the left,  improve=114.6837, (0 missing)
##       kidney            < 0.5    to the left,  improve=108.9096, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the left,  agree=0.542, adj=0.063, (0 split)
##       arthritis  < 0.5    to the left,  agree=0.539, adj=0.058, (0 split)
##       ihd        < 0.5    to the left,  agree=0.534, adj=0.048, (0 split)
##       kidney     < 0.5    to the left,  agree=0.532, adj=0.044, (0 split)
##       diabetes   < 0.5    to the left,  agree=0.532, adj=0.043, (0 split)
## 
## Node number 7: 69518 observations,    complexity param=0.01395884
##   predicted class=B2  expected loss=0.5995138  P(node) =0.2529739
##     class counts: 18972 27841 13728  7809  1168
##    probabilities: 0.273 0.400 0.197 0.112 0.017 
##   left son=14 (15717 obs) right son=15 (53801 obs)
##   Primary splits:
##       diabetes      < 0.5    to the left,  improve=646.4740, (0 missing)
##       kidney        < 0.5    to the left,  improve=604.0313, (0 missing)
##       arthritis     < 0.5    to the left,  improve=501.1263, (0 missing)
##       ihd           < 0.5    to the left,  improve=427.9009, (0 missing)
##       heart.failure < 0.5    to the left,  improve=380.0080, (0 missing)
## 
## Node number 12: 20077 observations,    complexity param=9.409212e-05
##   predicted class=B1  expected loss=0.4247148  P(node) =0.07305961
##     class counts: 11550  5416  2200   834    77
##    probabilities: 0.575 0.270 0.110 0.042 0.004 
##   left son=24 (8826 obs) right son=25 (11251 obs)
##   Primary splits:
##       diabetes      < 0.5    to the left,  improve=62.34344, (0 missing)
##       kidney        < 0.5    to the left,  improve=42.15624, (0 missing)
##       ihd           < 0.5    to the left,  improve=40.01287, (0 missing)
##       heart.failure < 0.5    to the left,  improve=36.00697, (0 missing)
##       arthritis     < 0.5    to the left,  improve=33.77686, (0 missing)
##   Surrogate splits:
##       ihd < 0.5    to the left,  agree=0.588, adj=0.062, (0 split)
## 
## Node number 13: 19221 observations,    complexity param=0.0006789392
##   predicted class=B1  expected loss=0.5372249  P(node) =0.06994465
##     class counts:  8895  6718  2556   948   104
##    probabilities: 0.463 0.350 0.133 0.049 0.005 
##   left son=26 (7137 obs) right son=27 (12084 obs)
##   Primary splits:
##       diabetes      < 0.5    to the left,  improve=71.31724, (0 missing)
##       arthritis     < 0.5    to the left,  improve=61.00585, (0 missing)
##       ihd           < 0.5    to the left,  improve=55.20411, (0 missing)
##       heart.failure < 0.5    to the left,  improve=52.20163, (0 missing)
##       kidney        < 0.5    to the left,  improve=49.73230, (0 missing)
## 
## Node number 14: 15717 observations,    complexity param=0.004350377
##   predicted class=B1  expected loss=0.5704651  P(node) =0.0571937
##     class counts:  6751  5490  2365   999   112
##    probabilities: 0.430 0.349 0.150 0.064 0.007 
##   left son=28 (13123 obs) right son=29 (2594 obs)
##   Primary splits:
##       cancer       < 0.5    to the left,  improve=130.12270, (0 missing)
##       arthritis    < 0.5    to the left,  improve=125.41530, (0 missing)
##       ihd          < 0.5    to the left,  improve= 80.76118, (0 missing)
##       depression   < 0.5    to the left,  improve= 61.32779, (0 missing)
##       osteoporosis < 0.5    to the left,  improve= 44.50253, (0 missing)
## 
## Node number 15: 53801 observations,    complexity param=0.0009879673
##   predicted class=B2  expected loss=0.5845616  P(node) =0.1957802
##     class counts: 12221 22351 11363  6810  1056
##    probabilities: 0.227 0.415 0.211 0.127 0.020 
##   left son=30 (25067 obs) right son=31 (28734 obs)
##   Primary splits:
##       kidney            < 0.5    to the left,  improve=408.9756, (0 missing)
##       reimbursement2008 < 15395  to the left,  improve=327.1281, (0 missing)
##       bucket2008        < 3.5    to the left,  improve=313.8191, (0 missing)
##       arthritis         < 0.5    to the left,  improve=266.5595, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=209.4718, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 8365   to the left,  agree=0.666, adj=0.282, (0 split)
##       bucket2008        < 2.5    to the left,  agree=0.664, adj=0.279, (0 split)
##       heart.failure     < 0.5    to the left,  agree=0.628, adj=0.201, (0 split)
##       copd              < 0.5    to the left,  agree=0.595, adj=0.132, (0 split)
##       ihd               < 0.5    to the left,  agree=0.575, adj=0.089, (0 split)
## 
## Node number 24: 8826 observations
##   predicted class=B1  expected loss=0.3716293  P(node) =0.03211755
##     class counts:  5546  2137   805   312    26
##    probabilities: 0.628 0.242 0.091 0.035 0.003 
## 
## Node number 25: 11251 observations,    complexity param=9.409212e-05
##   predicted class=B1  expected loss=0.4663585  P(node) =0.04094206
##     class counts:  6004  3279  1395   522    51
##    probabilities: 0.534 0.291 0.124 0.046 0.005 
##   left son=50 (9007 obs) right son=51 (2244 obs)
##   Primary splits:
##       kidney            < 0.5    to the left,  improve=18.86048, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=17.29926, (0 missing)
##       arthritis         < 0.5    to the left,  improve=16.91283, (0 missing)
##       reimbursement2008 < 1875   to the left,  improve=16.48954, (0 missing)
##       cancer            < 0.5    to the left,  improve=14.98495, (0 missing)
## 
## Node number 26: 7137 observations,    complexity param=0.0001051618
##   predicted class=B1  expected loss=0.470786  P(node) =0.02597133
##     class counts:  3777  2233   794   300    33
##    probabilities: 0.529 0.313 0.111 0.042 0.005 
##   left son=52 (5554 obs) right son=53 (1583 obs)
##   Primary splits:
##       arthritis  < 0.5    to the left,  improve=24.840370, (0 missing)
##       depression < 0.5    to the left,  improve=16.217060, (0 missing)
##       ihd        < 0.5    to the left,  improve=13.895180, (0 missing)
##       copd       < 0.5    to the left,  improve=12.688930, (0 missing)
##       kidney     < 0.5    to the left,  improve= 9.728645, (0 missing)
## 
## Node number 27: 12084 observations,    complexity param=0.0006789392
##   predicted class=B1  expected loss=0.5764647  P(node) =0.04397332
##     class counts:  5118  4485  1762   648    71
##    probabilities: 0.424 0.371 0.146 0.054 0.006 
##   left son=54 (8413 obs) right son=55 (3671 obs)
##   Primary splits:
##       arthritis         < 0.5    to the left,  improve=27.83165, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=26.70933, (0 missing)
##       ihd               < 0.5    to the left,  improve=24.37311, (0 missing)
##       kidney            < 0.5    to the left,  improve=22.60183, (0 missing)
##       reimbursement2008 < 2655   to the left,  improve=21.75660, (0 missing)
## 
## Node number 28: 13123 observations,    complexity param=0.00359764
##   predicted class=B1  expected loss=0.5360055  P(node) =0.04775421
##     class counts:  6089  4435  1751   763    85
##    probabilities: 0.464 0.338 0.133 0.058 0.006 
##   left son=56 (9625 obs) right son=57 (3498 obs)
##   Primary splits:
##       arthritis     < 0.5    to the left,  improve=126.28480, (0 missing)
##       ihd           < 0.5    to the left,  improve= 70.76778, (0 missing)
##       depression    < 0.5    to the left,  improve= 68.94332, (0 missing)
##       osteoporosis  < 0.5    to the left,  improve= 46.31934, (0 missing)
##       heart.failure < 0.5    to the left,  improve= 30.26771, (0 missing)
## 
## Node number 29: 2594 observations,    complexity param=6.167383e-05
##   predicted class=B2  expected loss=0.5932922  P(node) =0.009439489
##     class counts:   662  1055   614   236    27
##    probabilities: 0.255 0.407 0.237 0.091 0.010 
##   left son=58 (1000 obs) right son=59 (1594 obs)
##   Primary splits:
##       reimbursement2008 < 5770   to the left,  improve=8.464458, (0 missing)
##       arthritis         < 0.5    to the left,  improve=7.371565, (0 missing)
##       ihd               < 0.5    to the left,  improve=5.410820, (0 missing)
##       copd              < 0.5    to the left,  improve=5.301788, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.070575, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.823, adj=0.542, (0 split)
## 
## Node number 30: 25067 observations,    complexity param=0.0009879673
##   predicted class=B2  expected loss=0.57091  P(node) =0.09121807
##     class counts:  7517 10756  4691  1917   186
##    probabilities: 0.300 0.429 0.187 0.076 0.007 
##   left son=60 (15178 obs) right son=61 (9889 obs)
##   Primary splits:
##       arthritis     < 0.5    to the left,  improve=169.25970, (0 missing)
##       cancer        < 0.5    to the left,  improve= 99.57556, (0 missing)
##       ihd           < 0.5    to the left,  improve= 68.28883, (0 missing)
##       depression    < 0.5    to the left,  improve= 61.94482, (0 missing)
##       heart.failure < 0.5    to the left,  improve= 42.19646, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 66495  to the left,  agree=0.606, adj=0.001, (0 split)
## 
## Node number 31: 28734 observations,    complexity param=0.0002036818
##   predicted class=B2  expected loss=0.5964711  P(node) =0.1045622
##     class counts:  4704 11595  6672  4893   870
##    probabilities: 0.164 0.404 0.232 0.170 0.030 
##   left son=62 (16249 obs) right son=63 (12485 obs)
##   Primary splits:
##       reimbursement2008 < 15395  to the left,  improve=177.49270, (0 missing)
##       bucket2008        < 3.5    to the left,  improve=170.28940, (0 missing)
##       arthritis         < 0.5    to the left,  improve=101.31920, (0 missing)
##       heart.failure     < 0.5    to the left,  improve= 62.82321, (0 missing)
##       ihd               < 0.5    to the left,  improve= 55.35075, (0 missing)
##   Surrogate splits:
##       bucket2008 < 3.5    to the left,  agree=0.924, adj=0.826, (0 split)
##       copd       < 0.5    to the left,  agree=0.609, adj=0.101, (0 split)
##       stroke     < 0.5    to the left,  agree=0.605, adj=0.091, (0 split)
##       cancer     < 0.5    to the left,  agree=0.580, adj=0.033, (0 split)
##       alzheimers < 0.5    to the left,  agree=0.569, adj=0.008, (0 split)
## 
## Node number 50: 9007 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.4490951  P(node) =0.03277621
##     class counts:  4962  2540  1087   378    40
##    probabilities: 0.551 0.282 0.121 0.042 0.004 
##   left son=100 (4935 obs) right son=101 (4072 obs)
##   Primary splits:
##       reimbursement2008 < 1875   to the left,  improve=14.670650, (0 missing)
##       cancer            < 0.5    to the left,  improve=12.077140, (0 missing)
##       arthritis         < 0.5    to the left,  improve= 9.470091, (0 missing)
##       heart.failure     < 0.5    to the left,  improve= 7.308909, (0 missing)
##       depression        < 0.5    to the left,  improve= 6.801973, (0 missing)
##   Surrogate splits:
##       stroke < 0.5    to the left,  agree=0.549, adj=0.003, (0 split)
##       age    < 29.5   to the right, agree=0.548, adj=0.001, (0 split)
## 
## Node number 51: 2244 observations,    complexity param=9.409212e-05
##   predicted class=B1  expected loss=0.5356506  P(node) =0.00816585
##     class counts:  1042   739   308   144    11
##    probabilities: 0.464 0.329 0.137 0.064 0.005 
##   left son=102 (992 obs) right son=103 (1252 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the left,  improve=7.795458, (0 missing)
##       arthritis         < 0.5    to the left,  improve=7.027320, (0 missing)
##       ihd               < 0.5    to the left,  improve=4.964222, (0 missing)
##       reimbursement2008 < 1735   to the left,  improve=4.132280, (0 missing)
##       cancer            < 0.5    to the left,  improve=3.835396, (0 missing)
##   Surrogate splits:
##       ihd < 0.5    to the left,  agree=0.565, adj=0.016, (0 split)
##       age < 33.5   to the left,  agree=0.559, adj=0.002, (0 split)
## 
## Node number 52: 5554 observations,    complexity param=5.165842e-05
##   predicted class=B1  expected loss=0.4449046  P(node) =0.02021084
##     class counts:  3083  1647   580   217    27
##    probabilities: 0.555 0.297 0.104 0.039 0.005 
##   left son=104 (2348 obs) right son=105 (3206 obs)
##   Primary splits:
##       ihd           < 0.5    to the left,  improve=13.118310, (0 missing)
##       depression    < 0.5    to the left,  improve=12.689550, (0 missing)
##       kidney        < 0.5    to the left,  improve= 9.684755, (0 missing)
##       copd          < 0.5    to the left,  improve= 9.145592, (0 missing)
##       heart.failure < 0.5    to the left,  improve= 8.228139, (0 missing)
##   Surrogate splits:
##       age               < 28.5   to the left,  agree=0.579, adj=0.004, (0 split)
##       reimbursement2008 < 2185   to the left,  agree=0.578, adj=0.001, (0 split)
## 
## Node number 53: 1583 observations,    complexity param=0.0001051618
##   predicted class=B1  expected loss=0.5615919  P(node) =0.00576049
##     class counts:   694   586   214    83     6
##    probabilities: 0.438 0.370 0.135 0.052 0.004 
##   left son=106 (1525 obs) right son=107 (58 obs)
##   Primary splits:
##       stroke            < 0.5    to the left,  improve=5.133391, (0 missing)
##       reimbursement2008 < 2725   to the left,  improve=3.164238, (0 missing)
##       cancer            < 0.5    to the left,  improve=2.451745, (0 missing)
##       copd              < 0.5    to the left,  improve=2.436381, (0 missing)
##       depression        < 0.5    to the left,  improve=1.979459, (0 missing)
## 
## Node number 54: 8413 observations,    complexity param=0.0004372516
##   predicted class=B1  expected loss=0.5530726  P(node) =0.03061466
##     class counts:  3760  2943  1225   438    47
##    probabilities: 0.447 0.350 0.146 0.052 0.006 
##   left son=108 (4375 obs) right son=109 (4038 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the left,  improve=25.12070, (0 missing)
##       ihd               < 0.5    to the left,  improve=19.50225, (0 missing)
##       kidney            < 0.5    to the left,  improve=18.23799, (0 missing)
##       depression        < 0.5    to the left,  improve=14.07225, (0 missing)
##       reimbursement2008 < 2615   to the left,  improve=12.21338, (0 missing)
##   Surrogate splits:
##       kidney     < 0.5    to the left,  agree=0.569, adj=0.103, (0 split)
##       copd       < 0.5    to the left,  agree=0.568, adj=0.100, (0 split)
##       alzheimers < 0.5    to the left,  agree=0.546, adj=0.054, (0 split)
##       ihd        < 0.5    to the left,  agree=0.544, adj=0.050, (0 split)
##       stroke     < 0.5    to the left,  agree=0.536, adj=0.034, (0 split)
## 
## Node number 55: 3671 observations,    complexity param=0.0002988809
##   predicted class=B2  expected loss=0.579951  P(node) =0.01335866
##     class counts:  1358  1542   537   210    24
##    probabilities: 0.370 0.420 0.146 0.057 0.007 
##   left son=110 (2068 obs) right son=111 (1603 obs)
##   Primary splits:
##       reimbursement2008 < 2665   to the left,  improve=10.442080, (0 missing)
##       cancer            < 0.5    to the left,  improve= 4.234333, (0 missing)
##       ihd               < 0.5    to the left,  improve= 4.129116, (0 missing)
##       kidney            < 0.5    to the left,  improve= 3.679214, (0 missing)
##       copd              < 0.5    to the left,  improve= 3.281268, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the left,  agree=0.638, adj=0.170, (0 split)
##       cancer     < 0.5    to the left,  agree=0.566, adj=0.006, (0 split)
##       age        < 26.5   to the right, agree=0.564, adj=0.001, (0 split)
##       stroke     < 0.5    to the left,  agree=0.564, adj=0.001, (0 split)
## 
## Node number 56: 9625 observations,    complexity param=0.0001217663
##   predicted class=B1  expected loss=0.4874805  P(node) =0.03502509
##     class counts:  4933  2954  1162   520    56
##    probabilities: 0.513 0.307 0.121 0.054 0.006 
##   left son=112 (3135 obs) right son=113 (6490 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=51.18602, (0 missing)
##       depression        < 0.5    to the left,  improve=46.82343, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=27.25528, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=25.54800, (0 missing)
##       reimbursement2008 < 6615   to the left,  improve=12.84564, (0 missing)
## 
## Node number 57: 3498 observations,    complexity param=0.0004372516
##   predicted class=B2  expected loss=0.5766152  P(node) =0.01272912
##     class counts:  1156  1481   589   243    29
##    probabilities: 0.330 0.423 0.168 0.069 0.008 
##   left son=114 (2340 obs) right son=115 (1158 obs)
##   Primary splits:
##       reimbursement2008 < 8525   to the left,  improve=12.263650, (0 missing)
##       depression        < 0.5    to the left,  improve=10.454350, (0 missing)
##       bucket2008        < 2.5    to the left,  improve= 9.052395, (0 missing)
##       copd              < 0.5    to the left,  improve= 8.848663, (0 missing)
##       ihd               < 0.5    to the left,  improve= 8.087092, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.979, adj=0.935, (0 split)
##       kidney     < 0.5    to the left,  agree=0.692, adj=0.069, (0 split)
##       stroke     < 0.5    to the left,  agree=0.680, adj=0.033, (0 split)
## 
## Node number 58: 1000 observations
##   predicted class=B2  expected loss=0.562  P(node) =0.00363897
##     class counts:   296   438   191    70     5
##    probabilities: 0.296 0.438 0.191 0.070 0.005 
## 
## Node number 59: 1594 observations,    complexity param=6.167383e-05
##   predicted class=B2  expected loss=0.6129235  P(node) =0.005800519
##     class counts:   366   617   423   166    22
##    probabilities: 0.230 0.387 0.265 0.104 0.014 
##   left son=118 (1054 obs) right son=119 (540 obs)
##   Primary splits:
##       reimbursement2008 < 8645   to the right, improve=7.014383, (0 missing)
##       arthritis         < 0.5    to the left,  improve=5.636989, (0 missing)
##       bucket2008        < 2.5    to the right, improve=4.256675, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=3.245615, (0 missing)
##       ihd               < 0.5    to the left,  improve=2.672736, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.949, adj=0.848, (0 split)
##       age        < 27.5   to the right, agree=0.662, adj=0.002, (0 split)
## 
## Node number 60: 15178 observations,    complexity param=0.0009879673
##   predicted class=B2  expected loss=0.6047569  P(node) =0.05523229
##     class counts:  5388  5999  2649  1047    95
##    probabilities: 0.355 0.395 0.175 0.069 0.006 
##   left son=120 (12572 obs) right son=121 (2606 obs)
##   Primary splits:
##       cancer        < 0.5    to the left,  improve=92.65854, (0 missing)
##       ihd           < 0.5    to the left,  improve=43.72992, (0 missing)
##       depression    < 0.5    to the left,  improve=36.05906, (0 missing)
##       heart.failure < 0.5    to the left,  improve=30.26654, (0 missing)
##       copd          < 0.5    to the left,  improve=25.73984, (0 missing)
## 
## Node number 61: 9889 observations,    complexity param=6.918538e-05
##   predicted class=B2  expected loss=0.5189605  P(node) =0.03598578
##     class counts:  2129  4757  2042   870    91
##    probabilities: 0.215 0.481 0.206 0.088 0.009 
##   left son=122 (5134 obs) right son=123 (4755 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=18.84327, (0 missing)
##       cancer            < 0.5    to the left,  improve=17.45891, (0 missing)
##       ihd               < 0.5    to the left,  improve=13.35120, (0 missing)
##       reimbursement2008 < 9795   to the left,  improve=12.33086, (0 missing)
##       copd              < 0.5    to the left,  improve=12.26415, (0 missing)
##   Surrogate splits:
##       alzheimers        < 0.5    to the left,  agree=0.564, adj=0.093, (0 split)
##       copd              < 0.5    to the left,  agree=0.546, adj=0.056, (0 split)
##       reimbursement2008 < 5815   to the left,  agree=0.542, adj=0.048, (0 split)
##       age               < 64.5   to the right, agree=0.537, adj=0.037, (0 split)
##       bucket2008        < 2.5    to the left,  agree=0.536, adj=0.036, (0 split)
## 
## Node number 62: 16249 observations,    complexity param=0.0001411382
##   predicted class=B2  expected loss=0.5619423  P(node) =0.05912963
##     class counts:  3113  7118  3819  1946   253
##    probabilities: 0.192 0.438 0.235 0.120 0.016 
##   left son=124 (9424 obs) right son=125 (6825 obs)
##   Primary splits:
##       arthritis         < 0.5    to the left,  improve=70.60653, (0 missing)
##       cancer            < 0.5    to the left,  improve=30.24922, (0 missing)
##       ihd               < 0.5    to the left,  improve=29.86941, (0 missing)
##       reimbursement2008 < 5665   to the left,  improve=23.89268, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=21.55872, (0 missing)
## 
## Node number 63: 12485 observations,    complexity param=0.0002036818
##   predicted class=B2  expected loss=0.6414097  P(node) =0.04543255
##     class counts:  1591  4477  2853  2947   617
##    probabilities: 0.127 0.359 0.229 0.236 0.049 
##   left son=126 (5402 obs) right son=127 (7083 obs)
##   Primary splits:
##       arthritis         < 0.5    to the right, improve=35.40534, (0 missing)
##       cancer            < 0.5    to the left,  improve=26.78171, (0 missing)
##       reimbursement2008 < 26625  to the left,  improve=24.60405, (0 missing)
##       depression        < 0.5    to the left,  improve=23.29796, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=17.01274, (0 missing)
##   Surrogate splits:
##       age               < 28.5   to the left,  agree=0.568, adj=0.002, (0 split)
##       reimbursement2008 < 15435  to the left,  agree=0.568, adj=0.001, (0 split)
## 
## Node number 100: 4935 observations
##   predicted class=B1  expected loss=0.4196555  P(node) =0.01795832
##     class counts:  2864  1294   550   205    22
##    probabilities: 0.580 0.262 0.111 0.042 0.004 
## 
## Node number 101: 4072 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.4847741  P(node) =0.01481789
##     class counts:  2098  1246   537   173    18
##    probabilities: 0.515 0.306 0.132 0.042 0.004 
##   left son=202 (3786 obs) right son=203 (286 obs)
##   Primary splits:
##       cancer        < 0.5    to the left,  improve=5.937439, (0 missing)
##       arthritis     < 0.5    to the left,  improve=5.625805, (0 missing)
##       copd          < 0.5    to the left,  improve=3.348444, (0 missing)
##       ihd           < 0.5    to the left,  improve=3.030239, (0 missing)
##       heart.failure < 0.5    to the left,  improve=2.851779, (0 missing)
## 
## Node number 102: 992 observations
##   predicted class=B1  expected loss=0.4808468  P(node) =0.003609859
##     class counts:   515   292   126    57     2
##    probabilities: 0.519 0.294 0.127 0.057 0.002 
## 
## Node number 103: 1252 observations,    complexity param=9.409212e-05
##   predicted class=B1  expected loss=0.5790735  P(node) =0.004555991
##     class counts:   527   447   182    87     9
##    probabilities: 0.421 0.357 0.145 0.069 0.007 
##   left son=206 (904 obs) right son=207 (348 obs)
##   Primary splits:
##       arthritis         < 0.5    to the left,  improve=7.739842, (0 missing)
##       age               < 93.5   to the left,  improve=3.754099, (0 missing)
##       cancer            < 0.5    to the left,  improve=3.514161, (0 missing)
##       reimbursement2008 < 1955   to the left,  improve=3.377454, (0 missing)
##       ihd               < 0.5    to the left,  improve=1.751139, (0 missing)
##   Surrogate splits:
##       age < 30.5   to the right, agree=0.724, adj=0.006, (0 split)
## 
## Node number 104: 2348 observations
##   predicted class=B1  expected loss=0.3973595  P(node) =0.008544303
##     class counts:  1415   632   217    72    12
##    probabilities: 0.603 0.269 0.092 0.031 0.005 
## 
## Node number 105: 3206 observations,    complexity param=5.165842e-05
##   predicted class=B1  expected loss=0.4797255  P(node) =0.01166654
##     class counts:  1668  1015   363   145    15
##    probabilities: 0.520 0.317 0.113 0.045 0.005 
##   left son=210 (2325 obs) right son=211 (881 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=8.135493, (0 missing)
##       kidney            < 0.5    to the left,  improve=5.219511, (0 missing)
##       reimbursement2008 < 2785   to the left,  improve=4.205524, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.201394, (0 missing)
##       copd              < 0.5    to the left,  improve=3.002159, (0 missing)
##   Surrogate splits:
##       age < 29.5   to the right, agree=0.726, adj=0.003, (0 split)
## 
## Node number 106: 1525 observations,    complexity param=0.0001051618
##   predicted class=B1  expected loss=0.5534426  P(node) =0.00554943
##     class counts:   681   554   202    82     6
##    probabilities: 0.447 0.363 0.132 0.054 0.004 
##   left son=212 (1438 obs) right son=213 (87 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=2.548424, (0 missing)
##       reimbursement2008 < 2715   to the right, improve=2.513748, (0 missing)
##       copd              < 0.5    to the left,  improve=1.973703, (0 missing)
##       depression        < 0.5    to the left,  improve=1.853940, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.632947, (0 missing)
## 
## Node number 107: 58 observations
##   predicted class=B2  expected loss=0.4482759  P(node) =0.0002110603
##     class counts:    13    32    12     1     0
##    probabilities: 0.224 0.552 0.207 0.017 0.000 
## 
## Node number 108: 4375 observations,    complexity param=0.0002435326
##   predicted class=B1  expected loss=0.5074286  P(node) =0.0159205
##     class counts:  2155  1478   555   170    17
##    probabilities: 0.493 0.338 0.127 0.039 0.004 
##   left son=216 (3992 obs) right son=217 (383 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=10.015540, (0 missing)
##       ihd               < 0.5    to the left,  improve= 9.488719, (0 missing)
##       depression        < 0.5    to the left,  improve= 7.316301, (0 missing)
##       reimbursement2008 < 2615   to the left,  improve= 5.949976, (0 missing)
##       copd              < 0.5    to the left,  improve= 5.117423, (0 missing)
## 
## Node number 109: 4038 observations,    complexity param=0.0004372516
##   predicted class=B1  expected loss=0.602526  P(node) =0.01469416
##     class counts:  1605  1465   670   268    30
##    probabilities: 0.397 0.363 0.166 0.066 0.007 
##   left son=218 (2819 obs) right son=219 (1219 obs)
##   Primary splits:
##       kidney            < 0.5    to the left,  improve=10.392200, (0 missing)
##       reimbursement2008 < 2455   to the left,  improve= 6.028802, (0 missing)
##       ihd               < 0.5    to the left,  improve= 5.795095, (0 missing)
##       depression        < 0.5    to the left,  improve= 5.214940, (0 missing)
##       stroke            < 0.5    to the left,  improve= 3.343262, (0 missing)
## 
## Node number 110: 2068 observations,    complexity param=0.0002767415
##   predicted class=B1  expected loss=0.5918762  P(node) =0.007525391
##     class counts:   844   817   280   117    10
##    probabilities: 0.408 0.395 0.135 0.057 0.005 
##   left son=220 (517 obs) right son=221 (1551 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=3.581883, (0 missing)
##       reimbursement2008 < 2305   to the left,  improve=3.255344, (0 missing)
##       cancer            < 0.5    to the left,  improve=3.097089, (0 missing)
##       age               < 54.5   to the left,  improve=1.964830, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.730688, (0 missing)
## 
## Node number 111: 1603 observations
##   predicted class=B2  expected loss=0.547723  P(node) =0.00583327
##     class counts:   514   725   257    93    14
##    probabilities: 0.321 0.452 0.160 0.058 0.009 
## 
## Node number 112: 3135 observations,    complexity param=7.19528e-05
##   predicted class=B1  expected loss=0.3974482  P(node) =0.01140817
##     class counts:  1889   825   298   113    10
##    probabilities: 0.603 0.263 0.095 0.036 0.003 
##   left son=224 (2292 obs) right son=225 (843 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=19.892930, (0 missing)
##       reimbursement2008 < 9505   to the right, improve=15.211730, (0 missing)
##       bucket2008        < 2.5    to the right, improve=13.054300, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=10.317040, (0 missing)
##       age               < 92.5   to the left,  improve= 3.244996, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 60755  to the left,  agree=0.731, adj=0.001, (0 split)
## 
## Node number 113: 6490 observations,    complexity param=0.0001217663
##   predicted class=B1  expected loss=0.5309707  P(node) =0.02361692
##     class counts:  3044  2129   864   407    46
##    probabilities: 0.469 0.328 0.133 0.063 0.007 
##   left son=226 (4266 obs) right son=227 (2224 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=22.130520, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=12.472230, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=12.135520, (0 missing)
##       reimbursement2008 < 6615   to the left,  improve=10.028930, (0 missing)
##       bucket2008        < 2.5    to the left,  improve= 8.000565, (0 missing)
##   Surrogate splits:
##       age               < 34.5   to the right, agree=0.658, adj=0.003, (0 split)
##       reimbursement2008 < 115145 to the left,  agree=0.658, adj=0.001, (0 split)
## 
## Node number 114: 2340 observations,    complexity param=5.313437e-05
##   predicted class=B2  expected loss=0.542735  P(node) =0.008515191
##     class counts:   720  1070   391   144    15
##    probabilities: 0.308 0.457 0.167 0.062 0.006 
##   left son=228 (1359 obs) right son=229 (981 obs)
##   Primary splits:
##       reimbursement2008 < 4645   to the left,  improve=5.782135, (0 missing)
##       ihd               < 0.5    to the left,  improve=5.431632, (0 missing)
##       depression        < 0.5    to the left,  improve=4.505952, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=3.336155, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=3.247654, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.613, adj=0.076, (0 split)
##       copd       < 0.5    to the left,  agree=0.606, adj=0.059, (0 split)
##       kidney     < 0.5    to the left,  agree=0.586, adj=0.013, (0 split)
##       age        < 91.5   to the left,  agree=0.585, adj=0.011, (0 split)
##       stroke     < 0.5    to the left,  agree=0.585, adj=0.009, (0 split)
## 
## Node number 115: 1158 observations,    complexity param=0.0004372516
##   predicted class=B1  expected loss=0.6234888  P(node) =0.004213928
##     class counts:   436   411   198    99    14
##    probabilities: 0.377 0.355 0.171 0.085 0.012 
##   left son=230 (714 obs) right son=231 (444 obs)
##   Primary splits:
##       copd              < 0.5    to the left,  improve=13.168040, (0 missing)
##       depression        < 0.5    to the left,  improve= 8.948306, (0 missing)
##       kidney            < 0.5    to the left,  improve= 6.276303, (0 missing)
##       ihd               < 0.5    to the left,  improve= 5.293866, (0 missing)
##       reimbursement2008 < 14980  to the left,  improve= 4.056180, (0 missing)
##   Surrogate splits:
##       age               < 94.5   to the left,  agree=0.626, adj=0.025, (0 split)
##       reimbursement2008 < 72745  to the left,  agree=0.620, adj=0.009, (0 split)
## 
## Node number 118: 1054 observations,    complexity param=6.167383e-05
##   predicted class=B2  expected loss=0.6223909  P(node) =0.003835475
##     class counts:   281   398   250   109    16
##    probabilities: 0.267 0.378 0.237 0.103 0.015 
##   left son=236 (745 obs) right son=237 (309 obs)
##   Primary splits:
##       arthritis         < 0.5    to the left,  improve=8.492364, (0 missing)
##       ihd               < 0.5    to the left,  improve=3.739184, (0 missing)
##       depression        < 0.5    to the left,  improve=2.714506, (0 missing)
##       copd              < 0.5    to the left,  improve=2.704564, (0 missing)
##       reimbursement2008 < 67610  to the left,  improve=2.665770, (0 missing)
##   Surrogate splits:
##       age < 29.5   to the right, agree=0.708, adj=0.003, (0 split)
## 
## Node number 119: 540 observations,    complexity param=6.167383e-05
##   predicted class=B2  expected loss=0.5944444  P(node) =0.001965044
##     class counts:    85   219   173    57     6
##    probabilities: 0.157 0.406 0.320 0.106 0.011 
##   left son=238 (243 obs) right son=239 (297 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the right, improve=3.144781, (0 missing)
##       reimbursement2008 < 7455   to the left,  improve=1.665302, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.352183, (0 missing)
##       age               < 86.5   to the right, improve=1.232072, (0 missing)
##       arthritis         < 0.5    to the right, improve=1.028824, (0 missing)
##   Surrogate splits:
##       copd       < 0.5    to the right, agree=0.604, adj=0.119, (0 split)
##       kidney     < 0.5    to the right, agree=0.585, adj=0.078, (0 split)
##       stroke     < 0.5    to the right, agree=0.583, adj=0.074, (0 split)
##       arthritis  < 0.5    to the right, agree=0.576, adj=0.058, (0 split)
##       depression < 0.5    to the right, agree=0.572, adj=0.049, (0 split)
## 
## Node number 120: 12572 observations,    complexity param=0.0009879673
##   predicted class=B2  expected loss=0.613188  P(node) =0.04574914
##     class counts:  4844  4863  2000   791    74
##    probabilities: 0.385 0.387 0.159 0.063 0.006 
##   left son=240 (2617 obs) right son=241 (9955 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=36.80981, (0 missing)
##       depression        < 0.5    to the left,  improve=36.47326, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=27.52215, (0 missing)
##       copd              < 0.5    to the left,  improve=21.85222, (0 missing)
##       reimbursement2008 < 8955   to the left,  improve=19.34797, (0 missing)
## 
## Node number 121: 2606 observations
##   predicted class=B2  expected loss=0.5640829  P(node) =0.009483157
##     class counts:   544  1136   649   256    21
##    probabilities: 0.209 0.436 0.249 0.098 0.008 
## 
## Node number 122: 5134 observations,    complexity param=6.918538e-05
##   predicted class=B2  expected loss=0.5190884  P(node) =0.01868247
##     class counts:  1277  2469   936   412    40
##    probabilities: 0.249 0.481 0.182 0.080 0.008 
##   left son=244 (4305 obs) right son=245 (829 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=12.348810, (0 missing)
##       reimbursement2008 < 9985   to the left,  improve=11.590150, (0 missing)
##       bucket2008        < 2.5    to the left,  improve= 7.979608, (0 missing)
##       ihd               < 0.5    to the left,  improve= 7.512372, (0 missing)
##       copd              < 0.5    to the left,  improve= 7.186891, (0 missing)
## 
## Node number 123: 4755 observations
##   predicted class=B2  expected loss=0.5188223  P(node) =0.0173033
##     class counts:   852  2288  1106   458    51
##    probabilities: 0.179 0.481 0.233 0.096 0.011 
## 
## Node number 124: 9424 observations,    complexity param=0.0001411382
##   predicted class=B2  expected loss=0.5992148  P(node) =0.03429366
##     class counts:  2192  3777  2139  1156   160
##    probabilities: 0.233 0.401 0.227 0.123 0.017 
##   left son=248 (7786 obs) right son=249 (1638 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=31.06099, (0 missing)
##       ihd               < 0.5    to the left,  improve=19.93184, (0 missing)
##       depression        < 0.5    to the left,  improve=16.57581, (0 missing)
##       reimbursement2008 < 6325   to the left,  improve=12.91187, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=10.82187, (0 missing)
## 
## Node number 125: 6825 observations
##   predicted class=B2  expected loss=0.5104762  P(node) =0.02483597
##     class counts:   921  3341  1680   790    93
##    probabilities: 0.135 0.490 0.246 0.116 0.014 
## 
## Node number 126: 5402 observations,    complexity param=7.748763e-05
##   predicted class=B2  expected loss=0.5960755  P(node) =0.01965772
##     class counts:   509  2182  1310  1186   215
##    probabilities: 0.094 0.404 0.243 0.220 0.040 
##   left son=252 (3345 obs) right son=253 (2057 obs)
##   Primary splits:
##       reimbursement2008 < 34925  to the left,  improve=14.212070, (0 missing)
##       copd              < 0.5    to the left,  improve=10.384850, (0 missing)
##       depression        < 0.5    to the left,  improve= 8.104595, (0 missing)
##       cancer            < 0.5    to the right, improve= 6.743072, (0 missing)
##       heart.failure     < 0.5    to the left,  improve= 6.417519, (0 missing)
##   Surrogate splits:
##       bucket2008 < 4.5    to the left,  agree=0.776, adj=0.413, (0 split)
## 
## Node number 127: 7083 observations,    complexity param=0.0002036818
##   predicted class=B2  expected loss=0.6759848  P(node) =0.02577483
##     class counts:  1082  2295  1543  1761   402
##    probabilities: 0.153 0.324 0.218 0.249 0.057 
##   left son=254 (5298 obs) right son=255 (1785 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=21.09129, (0 missing)
##       depression        < 0.5    to the left,  improve=19.29947, (0 missing)
##       reimbursement2008 < 26625  to the left,  improve=15.18952, (0 missing)
##       copd              < 0.5    to the left,  improve=14.68870, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=12.81802, (0 missing)
## 
## Node number 202: 3786 observations
##   predicted class=B1  expected loss=0.4772847  P(node) =0.01377714
##     class counts:  1979  1131   501   157    18
##    probabilities: 0.523 0.299 0.132 0.041 0.005 
## 
## Node number 203: 286 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5839161  P(node) =0.001040746
##     class counts:   119   115    36    16     0
##    probabilities: 0.416 0.402 0.126 0.056 0.000 
##   left son=406 (128 obs) right son=407 (158 obs)
##   Primary splits:
##       age               < 73.5   to the left,  improve=2.9724540, (0 missing)
##       reimbursement2008 < 2005   to the left,  improve=1.9802050, (0 missing)
##       depression        < 0.5    to the left,  improve=0.5460014, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.4144954, (0 missing)
##       ihd               < 0.5    to the left,  improve=0.3767582, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 1945   to the left,  agree=0.580, adj=0.063, (0 split)
##       arthritis         < 0.5    to the right, agree=0.563, adj=0.023, (0 split)
## 
## Node number 206: 904 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5376106  P(node) =0.003289629
##     class counts:   418   304   119    57     6
##    probabilities: 0.462 0.336 0.132 0.063 0.007 
##   left son=412 (270 obs) right son=413 (634 obs)
##   Primary splits:
##       reimbursement2008 < 1735   to the left,  improve=3.8438620, (0 missing)
##       age               < 93.5   to the left,  improve=3.6681650, (0 missing)
##       ihd               < 0.5    to the left,  improve=3.2669730, (0 missing)
##       cancer            < 0.5    to the left,  improve=3.0869480, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.7446912, (0 missing)
##   Surrogate splits:
##       age < 29     to the left,  agree=0.702, adj=0.004, (0 split)
## 
## Node number 207: 348 observations
##   predicted class=B2  expected loss=0.5890805  P(node) =0.001266362
##     class counts:   109   143    63    30     3
##    probabilities: 0.313 0.411 0.181 0.086 0.009 
## 
## Node number 210: 2325 observations
##   predicted class=B1  expected loss=0.4541935  P(node) =0.008460606
##     class counts:  1269   700   245    99    12
##    probabilities: 0.546 0.301 0.105 0.043 0.005 
## 
## Node number 211: 881 observations,    complexity param=5.165842e-05
##   predicted class=B1  expected loss=0.5471056  P(node) =0.003205933
##     class counts:   399   315   118    46     3
##    probabilities: 0.453 0.358 0.134 0.052 0.003 
##   left son=422 (763 obs) right son=423 (118 obs)
##   Primary splits:
##       kidney            < 0.5    to the left,  improve=3.122415, (0 missing)
##       age               < 78.5   to the right, improve=2.656467, (0 missing)
##       reimbursement2008 < 2205   to the right, improve=1.600090, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.074836, (0 missing)
##       bucket2008        < 1.5    to the left,  improve=1.071176, (0 missing)
## 
## Node number 212: 1438 observations,    complexity param=8.855729e-05
##   predicted class=B1  expected loss=0.5452017  P(node) =0.00523284
##     class counts:   654   515   187    76     6
##    probabilities: 0.455 0.358 0.130 0.053 0.004 
##   left son=424 (495 obs) right son=425 (943 obs)
##   Primary splits:
##       reimbursement2008 < 2715   to the right, improve=2.835023, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.879898, (0 missing)
##       copd              < 0.5    to the left,  improve=1.857999, (0 missing)
##       age               < 40.5   to the right, improve=1.802592, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.761837, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the right, agree=0.713, adj=0.166, (0 split)
##       age        < 28.5   to the left,  agree=0.656, adj=0.002, (0 split)
## 
## Node number 213: 87 observations
##   predicted class=B2  expected loss=0.5517241  P(node) =0.0003165904
##     class counts:    27    39    15     6     0
##    probabilities: 0.310 0.448 0.172 0.069 0.000 
## 
## Node number 216: 3992 observations,    complexity param=6.918538e-05
##   predicted class=B1  expected loss=0.495491  P(node) =0.01452677
##     class counts:  2014  1315   497   153    13
##    probabilities: 0.505 0.329 0.124 0.038 0.003 
##   left son=432 (1265 obs) right son=433 (2727 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=7.867939, (0 missing)
##       depression        < 0.5    to the left,  improve=6.016589, (0 missing)
##       copd              < 0.5    to the left,  improve=5.402587, (0 missing)
##       kidney            < 0.5    to the left,  improve=3.916699, (0 missing)
##       reimbursement2008 < 2615   to the left,  improve=3.836002, (0 missing)
## 
## Node number 217: 383 observations,    complexity param=0.0001826494
##   predicted class=B2  expected loss=0.5744125  P(node) =0.001393726
##     class counts:   141   163    58    17     4
##    probabilities: 0.368 0.426 0.151 0.044 0.010 
##   left son=434 (238 obs) right son=435 (145 obs)
##   Primary splits:
##       reimbursement2008 < 2705   to the left,  improve=4.9624930, (0 missing)
##       depression        < 0.5    to the left,  improve=3.2303380, (0 missing)
##       age               < 67.5   to the right, improve=2.3511250, (0 missing)
##       ihd               < 0.5    to the left,  improve=1.5735720, (0 missing)
##       bucket2008        < 1.5    to the left,  improve=0.9813303, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the left,  agree=0.681, adj=0.159, (0 split)
##       age        < 45.5   to the right, agree=0.624, adj=0.007, (0 split)
## 
## Node number 218: 2819 observations,    complexity param=0.0001129105
##   predicted class=B1  expected loss=0.5746719  P(node) =0.01025826
##     class counts:  1199   980   439   183    18
##    probabilities: 0.425 0.348 0.156 0.065 0.006 
##   left son=436 (635 obs) right son=437 (2184 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=6.072389, (0 missing)
##       reimbursement2008 < 2325   to the left,  improve=3.797765, (0 missing)
##       age               < 40.5   to the right, improve=3.110525, (0 missing)
##       depression        < 0.5    to the left,  improve=2.993563, (0 missing)
##       stroke            < 0.5    to the left,  improve=2.412511, (0 missing)
## 
## Node number 219: 1219 observations,    complexity param=8.855729e-05
##   predicted class=B2  expected loss=0.6021329  P(node) =0.004435905
##     class counts:   406   485   231    85    12
##    probabilities: 0.333 0.398 0.189 0.070 0.010 
##   left son=438 (613 obs) right son=439 (606 obs)
##   Primary splits:
##       reimbursement2008 < 2615   to the left,  improve=4.2080810, (0 missing)
##       age               < 98.5   to the right, improve=2.1482090, (0 missing)
##       depression        < 0.5    to the left,  improve=1.6601240, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.8099205, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=0.7434054, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the left,  agree=0.579, adj=0.153, (0 split)
##       depression < 0.5    to the left,  agree=0.523, adj=0.041, (0 split)
##       stroke     < 0.5    to the left,  agree=0.522, adj=0.038, (0 split)
##       age        < 65.5   to the right, agree=0.519, adj=0.033, (0 split)
##       cancer     < 0.5    to the left,  agree=0.514, adj=0.021, (0 split)
## 
## Node number 220: 517 observations,    complexity param=7.19528e-05
##   predicted class=B1  expected loss=0.5299807  P(node) =0.001881348
##     class counts:   243   191    57    25     1
##    probabilities: 0.470 0.369 0.110 0.048 0.002 
##   left son=440 (143 obs) right son=441 (374 obs)
##   Primary splits:
##       reimbursement2008 < 2295   to the left,  improve=6.0966680, (0 missing)
##       cancer            < 0.5    to the left,  improve=2.5628030, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.9493160, (0 missing)
##       age               < 44.5   to the right, improve=1.5968610, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.9005685, (0 missing)
##   Surrogate splits:
##       age < 98.5   to the right, agree=0.729, adj=0.021, (0 split)
## 
## Node number 221: 1551 observations,    complexity param=9.962695e-05
##   predicted class=B2  expected loss=0.5963894  P(node) =0.005644043
##     class counts:   601   626   223    92     9
##    probabilities: 0.387 0.404 0.144 0.059 0.006 
##   left son=442 (18 obs) right son=443 (1533 obs)
##   Primary splits:
##       age    < 35     to the left,  improve=3.0170030, (0 missing)
##       kidney < 0.5    to the left,  improve=2.3281310, (0 missing)
##       cancer < 0.5    to the left,  improve=1.5502140, (0 missing)
##       stroke < 0.5    to the left,  improve=1.1903410, (0 missing)
##       copd   < 0.5    to the left,  improve=0.9727402, (0 missing)
## 
## Node number 224: 2292 observations
##   predicted class=B1  expected loss=0.3582024  P(node) =0.00834052
##     class counts:  1471   549   183    79    10
##    probabilities: 0.642 0.240 0.080 0.034 0.004 
## 
## Node number 225: 843 observations,    complexity param=7.19528e-05
##   predicted class=B1  expected loss=0.5041518  P(node) =0.003067652
##     class counts:   418   276   115    34     0
##    probabilities: 0.496 0.327 0.136 0.040 0.000 
##   left son=450 (810 obs) right son=451 (33 obs)
##   Primary splits:
##       age               < 92.5   to the left,  improve=5.7055350, (0 missing)
##       reimbursement2008 < 11540  to the right, improve=5.6370950, (0 missing)
##       bucket2008        < 2.5    to the right, improve=2.9317810, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.7284423, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.3506867, (0 missing)
## 
## Node number 226: 4266 observations,    complexity param=0.0001051618
##   predicted class=B1  expected loss=0.4946085  P(node) =0.01552385
##     class counts:  2156  1343   503   238    26
##    probabilities: 0.505 0.315 0.118 0.056 0.006 
##   left son=452 (3304 obs) right son=453 (962 obs)
##   Primary splits:
##       osteoporosis      < 0.5    to the left,  improve=10.212680, (0 missing)
##       reimbursement2008 < 5905   to the right, improve= 9.673580, (0 missing)
##       bucket2008        < 2.5    to the right, improve= 7.844764, (0 missing)
##       heart.failure     < 0.5    to the left,  improve= 6.371374, (0 missing)
##       age               < 62.5   to the left,  improve= 3.683231, (0 missing)
## 
## Node number 227: 2224 observations,    complexity param=0.0001217663
##   predicted class=B1  expected loss=0.6007194  P(node) =0.00809307
##     class counts:   888   786   361   169    20
##    probabilities: 0.399 0.353 0.162 0.076 0.009 
##   left son=454 (1518 obs) right son=455 (706 obs)
##   Primary splits:
##       kidney            < 0.5    to the left,  improve=6.746609, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=4.569316, (0 missing)
##       reimbursement2008 < 10710  to the left,  improve=3.711923, (0 missing)
##       age               < 39.5   to the right, improve=3.285727, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=2.661027, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 14380  to the left,  agree=0.714, adj=0.101, (0 split)
##       bucket2008        < 3.5    to the left,  agree=0.708, adj=0.081, (0 split)
##       age               < 98.5   to the left,  agree=0.684, adj=0.004, (0 split)
## 
## Node number 228: 1359 observations,    complexity param=5.313437e-05
##   predicted class=B2  expected loss=0.5548197  P(node) =0.004945361
##     class counts:   467   605   203    76     8
##    probabilities: 0.344 0.445 0.149 0.056 0.006 
##   left son=456 (440 obs) right son=457 (919 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=3.300387, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=2.001264, (0 missing)
##       reimbursement2008 < 3265   to the left,  improve=1.998939, (0 missing)
##       depression        < 0.5    to the left,  improve=1.755319, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.574681, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 3095   to the left,  agree=0.678, adj=0.007, (0 split)
##       age               < 29.5   to the left,  agree=0.678, adj=0.005, (0 split)
## 
## Node number 229: 981 observations
##   predicted class=B2  expected loss=0.5259939  P(node) =0.00356983
##     class counts:   253   465   188    68     7
##    probabilities: 0.258 0.474 0.192 0.069 0.007 
## 
## Node number 230: 714 observations,    complexity param=0.0001881842
##   predicted class=B1  expected loss=0.5546218  P(node) =0.002598225
##     class counts:   318   239    91    61     5
##    probabilities: 0.445 0.335 0.127 0.085 0.007 
##   left son=460 (412 obs) right son=461 (302 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=8.699660, (0 missing)
##       age               < 92.5   to the right, improve=3.253447, (0 missing)
##       reimbursement2008 < 14980  to the left,  improve=2.826720, (0 missing)
##       bucket2008        < 3.5    to the left,  improve=2.191697, (0 missing)
##       kidney            < 0.5    to the left,  improve=2.037790, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 32685  to the left,  agree=0.591, adj=0.033, (0 split)
##       age               < 35.5   to the right, agree=0.583, adj=0.013, (0 split)
## 
## Node number 231: 444 observations,    complexity param=6.088314e-05
##   predicted class=B2  expected loss=0.6126126  P(node) =0.001615703
##     class counts:   118   172   107    38     9
##    probabilities: 0.266 0.387 0.241 0.086 0.020 
##   left son=462 (282 obs) right son=463 (162 obs)
##   Primary splits:
##       osteoporosis      < 0.5    to the left,  improve=3.735228, (0 missing)
##       kidney            < 0.5    to the left,  improve=3.274615, (0 missing)
##       reimbursement2008 < 68975  to the right, improve=3.185223, (0 missing)
##       ihd               < 0.5    to the left,  improve=3.085645, (0 missing)
##       age               < 76.5   to the left,  improve=1.652811, (0 missing)
##   Surrogate splits:
##       age               < 95.5   to the left,  agree=0.644, adj=0.025, (0 split)
##       reimbursement2008 < 8635   to the right, agree=0.637, adj=0.006, (0 split)
## 
## Node number 236: 745 observations,    complexity param=6.167383e-05
##   predicted class=B2  expected loss=0.6228188  P(node) =0.002711033
##     class counts:   232   281   150    72    10
##    probabilities: 0.311 0.377 0.201 0.097 0.013 
##   left son=472 (159 obs) right son=473 (586 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=3.002920, (0 missing)
##       reimbursement2008 < 58135  to the left,  improve=2.259882, (0 missing)
##       depression        < 0.5    to the left,  improve=2.111862, (0 missing)
##       bucket2008        < 4.5    to the left,  improve=1.991400, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.920660, (0 missing)
## 
## Node number 237: 309 observations,    complexity param=6.167383e-05
##   predicted class=B2  expected loss=0.6213592  P(node) =0.001124442
##     class counts:    49   117   100    37     6
##    probabilities: 0.159 0.379 0.324 0.120 0.019 
##   left son=474 (237 obs) right son=475 (72 obs)
##   Primary splits:
##       reimbursement2008 < 10960  to the right, improve=2.966323, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.571780, (0 missing)
##       age               < 90.5   to the left,  improve=1.407411, (0 missing)
##       copd              < 0.5    to the left,  improve=1.306020, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.907593, (0 missing)
## 
## Node number 238: 243 observations
##   predicted class=B2  expected loss=0.526749  P(node) =0.0008842698
##     class counts:    33   115    67    24     4
##    probabilities: 0.136 0.473 0.276 0.099 0.016 
## 
## Node number 239: 297 observations,    complexity param=6.167383e-05
##   predicted class=B3  expected loss=0.6430976  P(node) =0.001080774
##     class counts:    52   104   106    33     2
##    probabilities: 0.175 0.350 0.357 0.111 0.007 
##   left son=478 (226 obs) right son=479 (71 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=1.480103, (0 missing)
##       reimbursement2008 < 6875   to the right, improve=1.383473, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.357727, (0 missing)
##       age               < 54     to the left,  improve=1.263809, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.096200, (0 missing)
## 
## Node number 240: 2617 observations,    complexity param=0.0001439056
##   predicted class=B1  expected loss=0.5257929  P(node) =0.009523186
##     class counts:  1241   884   351   127    14
##    probabilities: 0.474 0.338 0.134 0.049 0.005 
##   left son=480 (403 obs) right son=481 (2214 obs)
##   Primary splits:
##       reimbursement2008 < 9400   to the right, improve=12.428110, (0 missing)
##       bucket2008        < 2.5    to the right, improve= 8.843694, (0 missing)
##       depression        < 0.5    to the left,  improve= 8.588030, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve= 8.405901, (0 missing)
##       alzheimers        < 0.5    to the left,  improve= 4.036896, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.947, adj=0.658, (0 split)
## 
## Node number 241: 9955 observations,    complexity param=0.0009879673
##   predicted class=B2  expected loss=0.6003014  P(node) =0.03622595
##     class counts:  3603  3979  1649   664    60
##    probabilities: 0.362 0.400 0.166 0.067 0.006 
##   left son=482 (5563 obs) right son=483 (4392 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=24.69099, (0 missing)
##       copd              < 0.5    to the left,  improve=17.49244, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=17.05734, (0 missing)
##       reimbursement2008 < 8955   to the left,  improve=14.88623, (0 missing)
##       bucket2008        < 2.5    to the left,  improve= 9.99202, (0 missing)
##   Surrogate splits:
##       alzheimers        < 0.5    to the left,  agree=0.574, adj=0.034, (0 split)
##       age               < 47.5   to the right, agree=0.565, adj=0.013, (0 split)
##       copd              < 0.5    to the left,  agree=0.564, adj=0.013, (0 split)
##       reimbursement2008 < 13565  to the left,  agree=0.561, adj=0.005, (0 split)
##       bucket2008        < 3.5    to the left,  agree=0.559, adj=0.001, (0 split)
## 
## Node number 244: 4305 observations,    complexity param=6.918538e-05
##   predicted class=B2  expected loss=0.524971  P(node) =0.01566577
##     class counts:  1149  2045   746   328    37
##    probabilities: 0.267 0.475 0.173 0.076 0.009 
##   left son=488 (1063 obs) right son=489 (3242 obs)
##   Primary splits:
##       reimbursement2008 < 9880   to the right, improve=11.346300, (0 missing)
##       bucket2008        < 2.5    to the right, improve= 8.562449, (0 missing)
##       ihd               < 0.5    to the left,  improve= 7.353611, (0 missing)
##       copd              < 0.5    to the left,  improve= 6.701463, (0 missing)
##       heart.failure     < 0.5    to the left,  improve= 3.881008, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.941, adj=0.762, (0 split)
## 
## Node number 245: 829 observations
##   predicted class=B2  expected loss=0.4885404  P(node) =0.003016707
##     class counts:   128   424   190    84     3
##    probabilities: 0.154 0.511 0.229 0.101 0.004 
## 
## Node number 248: 7786 observations,    complexity param=0.0001411382
##   predicted class=B2  expected loss=0.6050604  P(node) =0.02833302
##     class counts:  1982  3075  1667   929   133
##    probabilities: 0.255 0.395 0.214 0.119 0.017 
##   left son=496 (964 obs) right son=497 (6822 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=18.914850, (0 missing)
##       depression        < 0.5    to the left,  improve=16.457650, (0 missing)
##       reimbursement2008 < 6325   to the left,  improve=12.927220, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve= 9.344273, (0 missing)
##       bucket2008        < 2.5    to the left,  improve= 9.314433, (0 missing)
## 
## Node number 249: 1638 observations
##   predicted class=B2  expected loss=0.5714286  P(node) =0.005960634
##     class counts:   210   702   472   227    27
##    probabilities: 0.128 0.429 0.288 0.139 0.016 
## 
## Node number 252: 3345 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.580568  P(node) =0.01217236
##     class counts:   372  1403   837   632   101
##    probabilities: 0.111 0.419 0.250 0.189 0.030 
##   left son=504 (1291 obs) right son=505 (2054 obs)
##   Primary splits:
##       depression    < 0.5    to the left,  improve=6.733363, (0 missing)
##       copd          < 0.5    to the left,  improve=6.399894, (0 missing)
##       cancer        < 0.5    to the left,  improve=5.398776, (0 missing)
##       heart.failure < 0.5    to the left,  improve=3.401421, (0 missing)
##       age           < 31.5   to the right, improve=3.041832, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 15665  to the left,  agree=0.614, adj=0.001, (0 split)
## 
## Node number 253: 2057 observations,    complexity param=7.748763e-05
##   predicted class=B2  expected loss=0.6212931  P(node) =0.007485362
##     class counts:   137   779   473   554   114
##    probabilities: 0.067 0.379 0.230 0.269 0.055 
##   left son=506 (520 obs) right son=507 (1537 obs)
##   Primary splits:
##       copd          < 0.5    to the left,  improve=4.741452, (0 missing)
##       age           < 62.5   to the right, improve=3.709690, (0 missing)
##       cancer        < 0.5    to the right, improve=3.631891, (0 missing)
##       ihd           < 0.5    to the left,  improve=3.269099, (0 missing)
##       heart.failure < 0.5    to the left,  improve=3.168350, (0 missing)
##   Surrogate splits:
##       heart.failure < 0.5    to the left,  agree=0.751, adj=0.015, (0 split)
##       age           < 29     to the left,  agree=0.749, adj=0.008, (0 split)
##       ihd           < 0.5    to the left,  agree=0.749, adj=0.008, (0 split)
## 
## Node number 254: 5298 observations,    complexity param=0.0002036818
##   predicted class=B2  expected loss=0.689128  P(node) =0.01927927
##     class counts:   908  1647  1051  1364   328
##    probabilities: 0.171 0.311 0.198 0.257 0.062 
##   left son=508 (2489 obs) right son=509 (2809 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=18.17296, (0 missing)
##       reimbursement2008 < 22335  to the left,  improve=13.06444, (0 missing)
##       copd              < 0.5    to the left,  improve=11.53148, (0 missing)
##       ihd               < 0.5    to the left,  improve= 8.63716, (0 missing)
##       heart.failure     < 0.5    to the left,  improve= 8.42218, (0 missing)
##   Surrogate splits:
##       copd              < 0.5    to the left,  agree=0.579, adj=0.104, (0 split)
##       alzheimers        < 0.5    to the left,  agree=0.573, adj=0.092, (0 split)
##       ihd               < 0.5    to the left,  agree=0.545, adj=0.033, (0 split)
##       heart.failure     < 0.5    to the left,  agree=0.544, adj=0.030, (0 split)
##       reimbursement2008 < 16955  to the left,  agree=0.535, adj=0.010, (0 split)
## 
## Node number 255: 1785 observations
##   predicted class=B2  expected loss=0.6369748  P(node) =0.006495562
##     class counts:   174   648   492   397    74
##    probabilities: 0.097 0.363 0.276 0.222 0.041 
## 
## Node number 406: 128 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5  P(node) =0.0004657882
##     class counts:    64    42    14     8     0
##    probabilities: 0.500 0.328 0.109 0.062 0.000 
##   left son=812 (95 obs) right son=813 (33 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=3.3313600, (0 missing)
##       reimbursement2008 < 2155   to the left,  improve=2.1875000, (0 missing)
##       age               < 70.5   to the right, improve=1.5228130, (0 missing)
##       arthritis         < 0.5    to the left,  improve=1.1806970, (0 missing)
##       copd              < 0.5    to the left,  improve=0.4207762, (0 missing)
## 
## Node number 407: 158 observations
##   predicted class=B2  expected loss=0.5379747  P(node) =0.0005749573
##     class counts:    55    73    22     8     0
##    probabilities: 0.348 0.462 0.139 0.051 0.000 
## 
## Node number 412: 270 observations
##   predicted class=B1  expected loss=0.462963  P(node) =0.000982522
##     class counts:   145    73    36    15     1
##    probabilities: 0.537 0.270 0.133 0.056 0.004 
## 
## Node number 413: 634 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5694006  P(node) =0.002307107
##     class counts:   273   231    83    42     5
##    probabilities: 0.431 0.364 0.131 0.066 0.008 
##   left son=826 (596 obs) right son=827 (38 obs)
##   Primary splits:
##       age               < 91.5   to the left,  improve=3.6059530, (0 missing)
##       ihd               < 0.5    to the left,  improve=2.2411130, (0 missing)
##       reimbursement2008 < 1765   to the right, improve=2.0115470, (0 missing)
##       cancer            < 0.5    to the left,  improve=1.8824720, (0 missing)
##       depression        < 0.5    to the right, improve=0.5863526, (0 missing)
## 
## Node number 422: 763 observations
##   predicted class=B1  expected loss=0.5307995  P(node) =0.002776534
##     class counts:   358   260   102    40     3
##    probabilities: 0.469 0.341 0.134 0.052 0.004 
## 
## Node number 423: 118 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5338983  P(node) =0.0004293985
##     class counts:    41    55    16     6     0
##    probabilities: 0.347 0.466 0.136 0.051 0.000 
##   left son=846 (22 obs) right son=847 (96 obs)
##   Primary splits:
##       reimbursement2008 < 2865   to the right, improve=2.6611130, (0 missing)
##       copd              < 0.5    to the left,  improve=1.5528850, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.3108310, (0 missing)
##       bucket2008        < 1.5    to the left,  improve=1.2553930, (0 missing)
##       age               < 89.5   to the left,  improve=0.9696791, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the right, agree=0.873, adj=0.318, (0 split)
## 
## Node number 424: 495 observations,    complexity param=8.855729e-05
##   predicted class=B1  expected loss=0.5656566  P(node) =0.00180129
##     class counts:   215   202    50    26     2
##    probabilities: 0.434 0.408 0.101 0.053 0.004 
##   left son=848 (385 obs) right son=849 (110 obs)
##   Primary splits:
##       reimbursement2008 < 2795   to the right, improve=2.2427130, (0 missing)
##       age               < 73.5   to the left,  improve=1.5903260, (0 missing)
##       ihd               < 0.5    to the left,  improve=1.1717170, (0 missing)
##       depression        < 0.5    to the left,  improve=0.5724615, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.5572971, (0 missing)
## 
## Node number 425: 943 observations
##   predicted class=B1  expected loss=0.5344645  P(node) =0.003431549
##     class counts:   439   313   137    50     4
##    probabilities: 0.466 0.332 0.145 0.053 0.004 
## 
## Node number 432: 1265 observations
##   predicted class=B1  expected loss=0.4442688  P(node) =0.004603298
##     class counts:   703   367   147    44     4
##    probabilities: 0.556 0.290 0.116 0.035 0.003 
## 
## Node number 433: 2727 observations,    complexity param=6.918538e-05
##   predicted class=B1  expected loss=0.5192519  P(node) =0.009923472
##     class counts:  1311   948   350   109     9
##    probabilities: 0.481 0.348 0.128 0.040 0.003 
##   left son=866 (1499 obs) right son=867 (1228 obs)
##   Primary splits:
##       reimbursement2008 < 2615   to the left,  improve=4.028460, (0 missing)
##       age               < 54.5   to the left,  improve=3.426946, (0 missing)
##       copd              < 0.5    to the left,  improve=2.215284, (0 missing)
##       stroke            < 0.5    to the left,  improve=2.121876, (0 missing)
##       depression        < 0.5    to the left,  improve=1.918448, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the left,  agree=0.612, adj=0.139, (0 split)
##       age        < 97.5   to the left,  agree=0.552, adj=0.005, (0 split)
## 
## Node number 434: 238 observations,    complexity param=0.0001826494
##   predicted class=B1  expected loss=0.5714286  P(node) =0.000866075
##     class counts:   102    86    38     9     3
##    probabilities: 0.429 0.361 0.160 0.038 0.013 
##   left son=868 (167 obs) right son=869 (71 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=4.834875, (0 missing)
##       age               < 59.5   to the right, improve=2.461134, (0 missing)
##       ihd               < 0.5    to the left,  improve=1.909944, (0 missing)
##       reimbursement2008 < 2285   to the left,  improve=1.842456, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.113912, (0 missing)
##   Surrogate splits:
##       age < 97.5   to the left,  agree=0.718, adj=0.056, (0 split)
## 
## Node number 435: 145 observations
##   predicted class=B2  expected loss=0.4689655  P(node) =0.0005276507
##     class counts:    39    77    20     8     1
##    probabilities: 0.269 0.531 0.138 0.055 0.007 
## 
## Node number 436: 635 observations
##   predicted class=B1  expected loss=0.5023622  P(node) =0.002310746
##     class counts:   316   196    93    26     4
##    probabilities: 0.498 0.309 0.146 0.041 0.006 
## 
## Node number 437: 2184 observations,    complexity param=0.0001129105
##   predicted class=B1  expected loss=0.595696  P(node) =0.007947511
##     class counts:   883   784   346   157    14
##    probabilities: 0.404 0.359 0.158 0.072 0.006 
##   left son=874 (393 obs) right son=875 (1791 obs)
##   Primary splits:
##       reimbursement2008 < 2315   to the left,  improve=4.386891, (0 missing)
##       depression        < 0.5    to the left,  improve=4.376862, (0 missing)
##       age               < 39.5   to the right, improve=3.004733, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=2.391734, (0 missing)
##       stroke            < 0.5    to the left,  improve=2.171601, (0 missing)
## 
## Node number 438: 613 observations,    complexity param=8.302246e-05
##   predicted class=B1  expected loss=0.6182708  P(node) =0.002230689
##     class counts:   234   226   111    36     6
##    probabilities: 0.382 0.369 0.181 0.059 0.010 
##   left son=876 (180 obs) right son=877 (433 obs)
##   Primary splits:
##       osteoporosis      < 0.5    to the right, improve=1.4494640, (0 missing)
##       age               < 98.5   to the right, improve=1.3979840, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.9190213, (0 missing)
##       reimbursement2008 < 2275   to the left,  improve=0.8284921, (0 missing)
##       depression        < 0.5    to the left,  improve=0.7804891, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 2605   to the right, agree=0.713, adj=0.022, (0 split)
##       age               < 99.5   to the right, agree=0.711, adj=0.017, (0 split)
## 
## Node number 439: 606 observations
##   predicted class=B2  expected loss=0.5726073  P(node) =0.002205216
##     class counts:   172   259   120    49     6
##    probabilities: 0.284 0.427 0.198 0.081 0.010 
## 
## Node number 440: 143 observations
##   predicted class=B1  expected loss=0.3986014  P(node) =0.0005203728
##     class counts:    86    37    11     9     0
##    probabilities: 0.601 0.259 0.077 0.063 0.000 
## 
## Node number 441: 374 observations,    complexity param=7.19528e-05
##   predicted class=B1  expected loss=0.5802139  P(node) =0.001360975
##     class counts:   157   154    46    16     1
##    probabilities: 0.420 0.412 0.123 0.043 0.003 
##   left son=882 (25 obs) right son=883 (349 obs)
##   Primary splits:
##       reimbursement2008 < 2315   to the left,  improve=4.569334, (0 missing)
##       cancer            < 0.5    to the left,  improve=2.355946, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.361181, (0 missing)
##       age               < 90.5   to the right, improve=1.103565, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.082873, (0 missing)
## 
## Node number 442: 18 observations
##   predicted class=B1  expected loss=0.2777778  P(node) =6.550147e-05
##     class counts:    13     4     0     1     0
##    probabilities: 0.722 0.222 0.000 0.056 0.000 
## 
## Node number 443: 1533 observations,    complexity param=7.748763e-05
##   predicted class=B2  expected loss=0.5942596  P(node) =0.005578542
##     class counts:   588   622   223    91     9
##    probabilities: 0.384 0.406 0.145 0.059 0.006 
##   left son=886 (1101 obs) right son=887 (432 obs)
##   Primary splits:
##       kidney            < 0.5    to the left,  improve=2.2490350, (0 missing)
##       cancer            < 0.5    to the left,  improve=1.4724050, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.3260620, (0 missing)
##       reimbursement2008 < 2435   to the left,  improve=1.1404580, (0 missing)
##       copd              < 0.5    to the left,  improve=0.9660973, (0 missing)
## 
## Node number 450: 810 observations,    complexity param=5.258089e-05
##   predicted class=B1  expected loss=0.491358  P(node) =0.002947566
##     class counts:   412   257   108    33     0
##    probabilities: 0.509 0.317 0.133 0.041 0.000 
##   left son=900 (117 obs) right son=901 (693 obs)
##   Primary splits:
##       reimbursement2008 < 11525  to the right, improve=5.1504220, (0 missing)
##       bucket2008        < 2.5    to the right, improve=2.8801500, (0 missing)
##       age               < 34.5   to the left,  improve=1.2970260, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.8677994, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.5279869, (0 missing)
##   Surrogate splits:
##       bucket2008 < 3.5    to the right, agree=0.928, adj=0.504, (0 split)
## 
## Node number 451: 33 observations
##   predicted class=B2  expected loss=0.4242424  P(node) =0.000120086
##     class counts:     6    19     7     1     0
##    probabilities: 0.182 0.576 0.212 0.030 0.000 
## 
## Node number 452: 3304 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.4757869  P(node) =0.01202316
##     class counts:  1732   979   389   183    21
##    probabilities: 0.524 0.296 0.118 0.055 0.006 
##   left son=904 (1626 obs) right son=905 (1678 obs)
##   Primary splits:
##       reimbursement2008 < 5905   to the right, improve=9.971499, (0 missing)
##       bucket2008        < 2.5    to the right, improve=7.851328, (0 missing)
##       age               < 62.5   to the left,  improve=4.441278, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.580749, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.765354, (0 missing)
##   Surrogate splits:
##       bucket2008    < 2.5    to the right, agree=0.861, adj=0.717, (0 split)
##       kidney        < 0.5    to the right, agree=0.637, adj=0.262, (0 split)
##       heart.failure < 0.5    to the right, agree=0.608, adj=0.204, (0 split)
##       copd          < 0.5    to the right, agree=0.590, adj=0.166, (0 split)
##       alzheimers    < 0.5    to the right, agree=0.558, adj=0.102, (0 split)
## 
## Node number 453: 962 observations,    complexity param=0.0001051618
##   predicted class=B1  expected loss=0.5592516  P(node) =0.00350069
##     class counts:   424   364   114    55     5
##    probabilities: 0.441 0.378 0.119 0.057 0.005 
##   left son=906 (857 obs) right son=907 (105 obs)
##   Primary splits:
##       stroke            < 0.5    to the left,  improve=3.576264, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.114700, (0 missing)
##       reimbursement2008 < 59635  to the left,  improve=2.145451, (0 missing)
##       age               < 97.5   to the right, improve=1.742305, (0 missing)
##       copd              < 0.5    to the left,  improve=1.012750, (0 missing)
## 
## Node number 454: 1518 observations
##   predicted class=B1  expected loss=0.5685112  P(node) =0.005523957
##     class counts:   655   520   243    92     8
##    probabilities: 0.431 0.343 0.160 0.061 0.005 
## 
## Node number 455: 706 observations,    complexity param=0.0001162314
##   predicted class=B2  expected loss=0.6232295  P(node) =0.002569113
##     class counts:   233   266   118    77    12
##    probabilities: 0.330 0.377 0.167 0.109 0.017 
##   left son=910 (696 obs) right son=911 (10 obs)
##   Primary splits:
##       reimbursement2008 < 3155   to the right, improve=3.301177, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.232296, (0 missing)
##       copd              < 0.5    to the left,  improve=2.330270, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.835216, (0 missing)
##       age               < 92.5   to the right, improve=1.805094, (0 missing)
## 
## Node number 456: 440 observations,    complexity param=5.313437e-05
##   predicted class=B2  expected loss=0.5636364  P(node) =0.001601147
##     class counts:   177   192    49    20     2
##    probabilities: 0.402 0.436 0.111 0.045 0.005 
##   left son=912 (58 obs) right son=913 (382 obs)
##   Primary splits:
##       reimbursement2008 < 3155   to the left,  improve=3.3827400, (0 missing)
##       age               < 80.5   to the right, improve=2.1481460, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.6300393, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.5745512, (0 missing)
##       depression        < 0.5    to the right, improve=0.5547491, (0 missing)
## 
## Node number 457: 919 observations
##   predicted class=B2  expected loss=0.5505985  P(node) =0.003344214
##     class counts:   290   413   154    56     6
##    probabilities: 0.316 0.449 0.168 0.061 0.007 
## 
## Node number 460: 412 observations
##   predicted class=B1  expected loss=0.4757282  P(node) =0.001499256
##     class counts:   216   120    42    30     4
##    probabilities: 0.524 0.291 0.102 0.073 0.010 
## 
## Node number 461: 302 observations,    complexity param=7.748763e-05
##   predicted class=B2  expected loss=0.6059603  P(node) =0.001098969
##     class counts:   102   119    49    31     1
##    probabilities: 0.338 0.394 0.162 0.103 0.003 
##   left son=922 (9 obs) right son=923 (293 obs)
##   Primary splits:
##       age               < 92.5   to the right, improve=2.5766490, (0 missing)
##       stroke            < 0.5    to the right, improve=1.8961920, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.0608030, (0 missing)
##       reimbursement2008 < 32980  to the right, improve=1.0319510, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.7951977, (0 missing)
## 
## Node number 462: 282 observations,    complexity param=6.088314e-05
##   predicted class=B2  expected loss=0.6631206  P(node) =0.00102619
##     class counts:    88    95    71    23     5
##    probabilities: 0.312 0.337 0.252 0.082 0.018 
##   left son=924 (220 obs) right son=925 (62 obs)
##   Primary splits:
##       reimbursement2008 < 27390  to the left,  improve=2.933452, (0 missing)
##       age               < 79.5   to the left,  improve=2.171675, (0 missing)
##       bucket2008        < 4.5    to the right, improve=1.933271, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.142914, (0 missing)
##       ihd               < 0.5    to the left,  improve=1.125301, (0 missing)
##   Surrogate splits:
##       bucket2008 < 3.5    to the left,  agree=0.816, adj=0.161, (0 split)
## 
## Node number 463: 162 observations
##   predicted class=B2  expected loss=0.5246914  P(node) =0.0005895132
##     class counts:    30    77    36    15     4
##    probabilities: 0.185 0.475 0.222 0.093 0.025 
## 
## Node number 472: 159 observations,    complexity param=6.167383e-05
##   predicted class=B1  expected loss=0.591195  P(node) =0.0005785963
##     class counts:    65    51    33     8     2
##    probabilities: 0.409 0.321 0.208 0.050 0.013 
##   left son=944 (76 obs) right son=945 (83 obs)
##   Primary splits:
##       reimbursement2008 < 11995  to the right, improve=3.4294220, (0 missing)
##       age               < 65     to the left,  improve=1.4674530, (0 missing)
##       copd              < 0.5    to the left,  improve=1.0021090, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.7722947, (0 missing)
##       bucket2008        < 3.5    to the left,  improve=0.4091195, (0 missing)
##   Surrogate splits:
##       bucket2008    < 3.5    to the right, agree=0.673, adj=0.316, (0 split)
##       alzheimers    < 0.5    to the right, agree=0.591, adj=0.145, (0 split)
##       copd          < 0.5    to the right, agree=0.572, adj=0.105, (0 split)
##       heart.failure < 0.5    to the right, agree=0.560, adj=0.079, (0 split)
##       age           < 85.5   to the right, agree=0.541, adj=0.039, (0 split)
## 
## Node number 473: 586 observations
##   predicted class=B2  expected loss=0.6075085  P(node) =0.002132437
##     class counts:   167   230   117    64     8
##    probabilities: 0.285 0.392 0.200 0.109 0.014 
## 
## Node number 474: 237 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.5738397  P(node) =0.000862436
##     class counts:    35   101    72    26     3
##    probabilities: 0.148 0.426 0.304 0.110 0.013 
##   left son=948 (126 obs) right son=949 (111 obs)
##   Primary splits:
##       copd              < 0.5    to the left,  improve=2.2728500, (0 missing)
##       reimbursement2008 < 18275  to the left,  improve=1.9530690, (0 missing)
##       bucket2008        < 3.5    to the left,  improve=1.1622440, (0 missing)
##       age               < 75.5   to the right, improve=1.0471110, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.8993424, (0 missing)
##   Surrogate splits:
##       age           < 86.5   to the left,  agree=0.599, adj=0.144, (0 split)
##       heart.failure < 0.5    to the left,  agree=0.586, adj=0.117, (0 split)
##       kidney        < 0.5    to the left,  agree=0.582, adj=0.108, (0 split)
##       depression    < 0.5    to the left,  agree=0.570, adj=0.081, (0 split)
##       stroke        < 0.5    to the left,  agree=0.565, adj=0.072, (0 split)
## 
## Node number 475: 72 observations
##   predicted class=B3  expected loss=0.6111111  P(node) =0.0002620059
##     class counts:    14    16    28    11     3
##    probabilities: 0.194 0.222 0.389 0.153 0.042 
## 
## Node number 478: 226 observations
##   predicted class=B2  expected loss=0.6238938  P(node) =0.0008224073
##     class counts:    40    85    74    26     1
##    probabilities: 0.177 0.376 0.327 0.115 0.004 
## 
## Node number 479: 71 observations
##   predicted class=B3  expected loss=0.5492958  P(node) =0.0002583669
##     class counts:    12    19    32     7     1
##    probabilities: 0.169 0.268 0.451 0.099 0.014 
## 
## Node number 480: 403 observations
##   predicted class=B1  expected loss=0.4243176  P(node) =0.001466505
##     class counts:   232    86    61    20     4
##    probabilities: 0.576 0.213 0.151 0.050 0.010 
## 
## Node number 481: 2214 observations,    complexity param=0.0001439056
##   predicted class=B1  expected loss=0.5442638  P(node) =0.008056681
##     class counts:  1009   798   290   107    10
##    probabilities: 0.456 0.360 0.131 0.048 0.005 
##   left son=962 (1636 obs) right son=963 (578 obs)
##   Primary splits:
##       osteoporosis < 0.5    to the left,  improve=6.829533, (0 missing)
##       depression   < 0.5    to the left,  improve=5.940363, (0 missing)
##       copd         < 0.5    to the left,  improve=3.544519, (0 missing)
##       alzheimers   < 0.5    to the left,  improve=2.874488, (0 missing)
##       age          < 57.5   to the left,  improve=2.182371, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 9185   to the left,  agree=0.742, adj=0.01, (0 split)
## 
## Node number 482: 5563 observations,    complexity param=0.000785946
##   predicted class=B1  expected loss=0.6002157  P(node) =0.02024359
##     class counts:  2224  2125   853   328    33
##    probabilities: 0.400 0.382 0.153 0.059 0.006 
##   left son=964 (1363 obs) right son=965 (4200 obs)
##   Primary splits:
##       reimbursement2008 < 8955   to the right, improve=10.881900, (0 missing)
##       heart.failure     < 0.5    to the left,  improve= 9.391652, (0 missing)
##       copd              < 0.5    to the left,  improve= 8.088751, (0 missing)
##       bucket2008        < 2.5    to the right, improve= 7.479912, (0 missing)
##       age               < 31.5   to the left,  improve= 2.090877, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.959, adj=0.834, (0 split)
##       age        < 28.5   to the left,  agree=0.755, adj=0.001, (0 split)
## 
## Node number 483: 4392 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.5778689  P(node) =0.01598236
##     class counts:  1379  1854   796   336    27
##    probabilities: 0.314 0.422 0.181 0.077 0.006 
##   left son=966 (2928 obs) right son=967 (1464 obs)
##   Primary splits:
##       reimbursement2008 < 8325   to the left,  improve=7.509791, (0 missing)
##       copd              < 0.5    to the left,  improve=6.714633, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=5.287260, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=5.126640, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=3.940785, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.979, adj=0.936, (0 split)
## 
## Node number 488: 1063 observations,    complexity param=6.918538e-05
##   predicted class=B2  expected loss=0.5983067  P(node) =0.003868226
##     class counts:   336   427   192    95    13
##    probabilities: 0.316 0.402 0.181 0.089 0.012 
##   left son=976 (102 obs) right son=977 (961 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=6.866573, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=4.371067, (0 missing)
##       copd              < 0.5    to the left,  improve=3.836869, (0 missing)
##       reimbursement2008 < 18130  to the left,  improve=3.632764, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=2.933358, (0 missing)
## 
## Node number 489: 3242 observations
##   predicted class=B2  expected loss=0.5009254  P(node) =0.01179754
##     class counts:   813  1618   554   233    24
##    probabilities: 0.251 0.499 0.171 0.072 0.007 
## 
## Node number 496: 964 observations,    complexity param=0.0001411382
##   predicted class=B1  expected loss=0.6307054  P(node) =0.003507968
##     class counts:   356   348   167    82    11
##    probabilities: 0.369 0.361 0.173 0.085 0.011 
##   left son=992 (572 obs) right son=993 (392 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=5.116701, (0 missing)
##       reimbursement2008 < 8255   to the left,  improve=4.417859, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=3.940989, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.162605, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=3.038248, (0 missing)
##   Surrogate splits:
##       stroke            < 0.5    to the left,  agree=0.601, adj=0.018, (0 split)
##       reimbursement2008 < 14855  to the left,  agree=0.598, adj=0.010, (0 split)
##       copd              < 0.5    to the left,  agree=0.594, adj=0.003, (0 split)
## 
## Node number 497: 6822 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.6002639  P(node) =0.02482506
##     class counts:  1626  2727  1500   847   122
##    probabilities: 0.238 0.400 0.220 0.124 0.018 
##   left son=994 (3172 obs) right son=995 (3650 obs)
##   Primary splits:
##       reimbursement2008 < 6325   to the left,  improve=11.481700, (0 missing)
##       depression        < 0.5    to the left,  improve=11.166950, (0 missing)
##       bucket2008        < 2.5    to the left,  improve= 7.602059, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve= 6.404955, (0 missing)
##       copd              < 0.5    to the left,  improve= 5.945024, (0 missing)
##   Surrogate splits:
##       bucket2008    < 2.5    to the left,  agree=0.869, adj=0.717, (0 split)
##       copd          < 0.5    to the left,  agree=0.576, adj=0.088, (0 split)
##       heart.failure < 0.5    to the left,  agree=0.570, adj=0.076, (0 split)
##       alzheimers    < 0.5    to the left,  agree=0.545, adj=0.020, (0 split)
##       age           < 31.5   to the left,  agree=0.536, adj=0.003, (0 split)
## 
## Node number 504: 1291 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5530596  P(node) =0.004697911
##     class counts:   183   577   280   219    32
##    probabilities: 0.142 0.447 0.217 0.170 0.025 
##   left son=1008 (973 obs) right son=1009 (318 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=5.737334, (0 missing)
##       reimbursement2008 < 32055  to the left,  improve=1.892346, (0 missing)
##       age               < 84.5   to the right, improve=1.763761, (0 missing)
##       ihd               < 0.5    to the left,  improve=1.710826, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.100420, (0 missing)
##   Surrogate splits:
##       age < 28.5   to the right, agree=0.755, adj=0.006, (0 split)
## 
## Node number 505: 2054 observations
##   predicted class=B2  expected loss=0.5978578  P(node) =0.007474445
##     class counts:   189   826   557   413    69
##    probabilities: 0.092 0.402 0.271 0.201 0.034 
## 
## Node number 506: 520 observations
##   predicted class=B2  expected loss=0.5769231  P(node) =0.001892265
##     class counts:    50   220   120   108    22
##    probabilities: 0.096 0.423 0.231 0.208 0.042 
## 
## Node number 507: 1537 observations,    complexity param=7.748763e-05
##   predicted class=B2  expected loss=0.6363045  P(node) =0.005593098
##     class counts:    87   559   353   446    92
##    probabilities: 0.057 0.364 0.230 0.290 0.060 
##   left son=1014 (1286 obs) right son=1015 (251 obs)
##   Primary splits:
##       age               < 62.5   to the right, improve=4.292573, (0 missing)
##       reimbursement2008 < 43950  to the left,  improve=3.358938, (0 missing)
##       cancer            < 0.5    to the right, improve=2.803709, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.956332, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.605851, (0 missing)
## 
## Node number 508: 2489 observations,    complexity param=0.0002036818
##   predicted class=B2  expected loss=0.7219767  P(node) =0.009057397
##     class counts:   541   692   436   670   150
##    probabilities: 0.217 0.278 0.175 0.269 0.060 
##   left son=1016 (1317 obs) right son=1017 (1172 obs)
##   Primary splits:
##       copd              < 0.5    to the right, improve=9.293163, (0 missing)
##       reimbursement2008 < 23175  to the left,  improve=7.265866, (0 missing)
##       ihd               < 0.5    to the left,  improve=7.177016, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=4.187307, (0 missing)
##       bucket2008        < 4.5    to the left,  improve=3.684093, (0 missing)
##   Surrogate splits:
##       heart.failure     < 0.5    to the right, agree=0.575, adj=0.097, (0 split)
##       alzheimers        < 0.5    to the right, agree=0.556, adj=0.057, (0 split)
##       ihd               < 0.5    to the right, agree=0.555, adj=0.055, (0 split)
##       reimbursement2008 < 27380  to the right, agree=0.544, adj=0.032, (0 split)
##       age               < 52.5   to the right, agree=0.532, adj=0.005, (0 split)
## 
## Node number 509: 2809 observations
##   predicted class=B2  expected loss=0.6600214  P(node) =0.01022187
##     class counts:   367   955   615   694   178
##    probabilities: 0.131 0.340 0.219 0.247 0.063 
## 
## Node number 812: 95 observations
##   predicted class=B1  expected loss=0.4315789  P(node) =0.0003457022
##     class counts:    54    25    11     5     0
##    probabilities: 0.568 0.263 0.116 0.053 0.000 
## 
## Node number 813: 33 observations
##   predicted class=B2  expected loss=0.4848485  P(node) =0.000120086
##     class counts:    10    17     3     3     0
##    probabilities: 0.303 0.515 0.091 0.091 0.000 
## 
## Node number 826: 596 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5536913  P(node) =0.002168826
##     class counts:   266   214    77    34     5
##    probabilities: 0.446 0.359 0.129 0.057 0.008 
##   left son=1652 (555 obs) right son=1653 (41 obs)
##   Primary splits:
##       reimbursement2008 < 1765   to the right, improve=2.4482060, (0 missing)
##       age               < 81.5   to the right, improve=2.3548750, (0 missing)
##       ihd               < 0.5    to the left,  improve=2.3213280, (0 missing)
##       cancer            < 0.5    to the left,  improve=2.1512770, (0 missing)
##       depression        < 0.5    to the left,  improve=0.5755867, (0 missing)
## 
## Node number 827: 38 observations
##   predicted class=B2  expected loss=0.5526316  P(node) =0.0001382809
##     class counts:     7    17     6     8     0
##    probabilities: 0.184 0.447 0.158 0.211 0.000 
## 
## Node number 846: 22 observations
##   predicted class=B1  expected loss=0.4545455  P(node) =8.005735e-05
##     class counts:    12     5     4     1     0
##    probabilities: 0.545 0.227 0.182 0.045 0.000 
## 
## Node number 847: 96 observations
##   predicted class=B2  expected loss=0.4791667  P(node) =0.0003493412
##     class counts:    29    50    12     5     0
##    probabilities: 0.302 0.521 0.125 0.052 0.000 
## 
## Node number 848: 385 observations,    complexity param=8.855729e-05
##   predicted class=B1  expected loss=0.5454545  P(node) =0.001401004
##     class counts:   175   146    39    23     2
##    probabilities: 0.455 0.379 0.101 0.060 0.005 
##   left son=1696 (263 obs) right son=1697 (122 obs)
##   Primary splits:
##       age               < 80.5   to the left,  improve=2.5496070, (0 missing)
##       ihd               < 0.5    to the left,  improve=1.7465940, (0 missing)
##       reimbursement2008 < 3025   to the right, improve=0.9395484, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.7038989, (0 missing)
##       depression        < 0.5    to the left,  improve=0.3133237, (0 missing)
## 
## Node number 849: 110 observations
##   predicted class=B2  expected loss=0.4909091  P(node) =0.0004002868
##     class counts:    40    56    11     3     0
##    probabilities: 0.364 0.509 0.100 0.027 0.000 
## 
## Node number 866: 1499 observations
##   predicted class=B1  expected loss=0.490994  P(node) =0.005454817
##     class counts:   763   492   189    52     3
##    probabilities: 0.509 0.328 0.126 0.035 0.002 
## 
## Node number 867: 1228 observations,    complexity param=6.918538e-05
##   predicted class=B1  expected loss=0.5537459  P(node) =0.004468656
##     class counts:   548   456   161    57     6
##    probabilities: 0.446 0.371 0.131 0.046 0.005 
##   left son=1734 (171 obs) right son=1735 (1057 obs)
##   Primary splits:
##       reimbursement2008 < 2995   to the right, improve=3.399761, (0 missing)
##       bucket2008        < 1.5    to the right, improve=3.399761, (0 missing)
##       age               < 83.5   to the left,  improve=2.697325, (0 missing)
##       kidney            < 0.5    to the left,  improve=2.465389, (0 missing)
##       depression        < 0.5    to the left,  improve=1.722272, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the right, agree=1, adj=1, (0 split)
## 
## Node number 868: 167 observations
##   predicted class=B1  expected loss=0.502994  P(node) =0.0006077081
##     class counts:    83    50    25     7     2
##    probabilities: 0.497 0.299 0.150 0.042 0.012 
## 
## Node number 869: 71 observations
##   predicted class=B2  expected loss=0.4929577  P(node) =0.0002583669
##     class counts:    19    36    13     2     1
##    probabilities: 0.268 0.507 0.183 0.028 0.014 
## 
## Node number 874: 393 observations
##   predicted class=B1  expected loss=0.5139949  P(node) =0.001430115
##     class counts:   191   134    46    20     2
##    probabilities: 0.486 0.341 0.117 0.051 0.005 
## 
## Node number 875: 1791 observations,    complexity param=0.0001129105
##   predicted class=B1  expected loss=0.6136237  P(node) =0.006517396
##     class counts:   692   650   300   137    12
##    probabilities: 0.386 0.363 0.168 0.076 0.007 
##   left son=1750 (1752 obs) right son=1751 (39 obs)
##   Primary splits:
##       age               < 39.5   to the right, improve=3.631907, (0 missing)
##       depression        < 0.5    to the left,  improve=3.598994, (0 missing)
##       reimbursement2008 < 2475   to the left,  improve=1.828499, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.790378, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.609073, (0 missing)
## 
## Node number 876: 180 observations,    complexity param=8.302246e-05
##   predicted class=B1  expected loss=0.5666667  P(node) =0.0006550147
##     class counts:    78    68    23    11     0
##    probabilities: 0.433 0.378 0.128 0.061 0.000 
##   left son=1752 (112 obs) right son=1753 (68 obs)
##   Primary splits:
##       reimbursement2008 < 2455   to the left,  improve=3.4787820, (0 missing)
##       age               < 88.5   to the left,  improve=1.3486290, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.8074074, (0 missing)
##       copd              < 0.5    to the left,  improve=0.6952328, (0 missing)
##       ihd               < 0.5    to the right, improve=0.3305556, (0 missing)
## 
## Node number 877: 433 observations,    complexity param=6.272808e-05
##   predicted class=B2  expected loss=0.6351039  P(node) =0.001575674
##     class counts:   156   158    88    25     6
##    probabilities: 0.360 0.365 0.203 0.058 0.014 
##   left son=1754 (403 obs) right son=1755 (30 obs)
##   Primary splits:
##       stroke            < 0.5    to the left,  improve=1.8988510, (0 missing)
##       age               < 45.5   to the left,  improve=1.3010460, (0 missing)
##       reimbursement2008 < 2255   to the left,  improve=1.2799960, (0 missing)
##       depression        < 0.5    to the left,  improve=1.2338070, (0 missing)
##       cancer            < 0.5    to the right, improve=0.6525541, (0 missing)
## 
## Node number 882: 25 observations
##   predicted class=B2  expected loss=0.24  P(node) =9.097426e-05
##     class counts:     6    19     0     0     0
##    probabilities: 0.240 0.760 0.000 0.000 0.000 
## 
## Node number 883: 349 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5673352  P(node) =0.001270001
##     class counts:   151   135    46    16     1
##    probabilities: 0.433 0.387 0.132 0.046 0.003 
##   left son=1766 (336 obs) right son=1767 (13 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=2.2574550, (0 missing)
##       age               < 69.5   to the left,  improve=1.5047970, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.0520090, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.8771249, (0 missing)
##       reimbursement2008 < 2535   to the right, improve=0.8193194, (0 missing)
## 
## Node number 886: 1101 observations,    complexity param=7.748763e-05
##   predicted class=B1  expected loss=0.595822  P(node) =0.004006506
##     class counts:   445   444   150    57     5
##    probabilities: 0.404 0.403 0.136 0.052 0.005 
##   left son=1772 (1057 obs) right son=1773 (44 obs)
##   Primary splits:
##       stroke            < 0.5    to the left,  improve=2.0669290, (0 missing)
##       age               < 46.5   to the left,  improve=1.0570040, (0 missing)
##       reimbursement2008 < 2535   to the right, improve=0.9632398, (0 missing)
##       cancer            < 0.5    to the left,  improve=0.9197684, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.7998904, (0 missing)
## 
## Node number 887: 432 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.587963  P(node) =0.001572035
##     class counts:   143   178    73    34     4
##    probabilities: 0.331 0.412 0.169 0.079 0.009 
##   left son=1774 (403 obs) right son=1775 (29 obs)
##   Primary splits:
##       reimbursement2008 < 2215   to the right, improve=1.979831, (0 missing)
##       depression        < 0.5    to the left,  improve=1.399095, (0 missing)
##       age               < 65.5   to the right, improve=1.336452, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.190308, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.062301, (0 missing)
## 
## Node number 900: 117 observations
##   predicted class=B1  expected loss=0.3418803  P(node) =0.0004257595
##     class counts:    77    25     8     7     0
##    probabilities: 0.658 0.214 0.068 0.060 0.000 
## 
## Node number 901: 693 observations,    complexity param=5.258089e-05
##   predicted class=B1  expected loss=0.5165945  P(node) =0.002521807
##     class counts:   335   232   100    26     0
##    probabilities: 0.483 0.335 0.144 0.038 0.000 
##   left son=1802 (684 obs) right son=1803 (9 obs)
##   Primary splits:
##       reimbursement2008 < 11105  to the left,  improve=2.2165640, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.2536740, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.8354877, (0 missing)
##       age               < 34     to the left,  improve=0.8168384, (0 missing)
##       copd              < 0.5    to the left,  improve=0.3325841, (0 missing)
## 
## Node number 904: 1626 observations
##   predicted class=B1  expected loss=0.4391144  P(node) =0.005916966
##     class counts:   912   414   190    99    11
##    probabilities: 0.561 0.255 0.117 0.061 0.007 
## 
## Node number 905: 1678 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.511323  P(node) =0.006106192
##     class counts:   820   565   199    84    10
##    probabilities: 0.489 0.337 0.119 0.050 0.006 
##   left son=1810 (1608 obs) right son=1811 (70 obs)
##   Primary splits:
##       reimbursement2008 < 5695   to the left,  improve=3.4228850, (0 missing)
##       age               < 54.5   to the left,  improve=3.4011680, (0 missing)
##       kidney            < 0.5    to the left,  improve=2.7930360, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.6256038, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.4872841, (0 missing)
## 
## Node number 906: 857 observations,    complexity param=8.855729e-05
##   predicted class=B1  expected loss=0.5425904  P(node) =0.003118598
##     class counts:   392   313   100    48     4
##    probabilities: 0.457 0.365 0.117 0.056 0.005 
##   left son=1812 (405 obs) right son=1813 (452 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the left,  improve=1.8446560, (0 missing)
##       reimbursement2008 < 8165   to the right, improve=1.5511950, (0 missing)
##       age               < 58.5   to the right, improve=1.3750490, (0 missing)
##       bucket2008        < 2.5    to the right, improve=1.0178390, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.9078092, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 7010   to the left,  agree=0.629, adj=0.215, (0 split)
##       bucket2008        < 2.5    to the left,  agree=0.611, adj=0.178, (0 split)
##       kidney            < 0.5    to the left,  agree=0.585, adj=0.121, (0 split)
##       copd              < 0.5    to the left,  agree=0.583, adj=0.119, (0 split)
##       age               < 77.5   to the left,  agree=0.555, adj=0.059, (0 split)
## 
## Node number 907: 105 observations
##   predicted class=B2  expected loss=0.5142857  P(node) =0.0003820919
##     class counts:    32    51    14     7     1
##    probabilities: 0.305 0.486 0.133 0.067 0.010 
## 
## Node number 910: 696 observations,    complexity param=0.0001162314
##   predicted class=B2  expected loss=0.6192529  P(node) =0.002532723
##     class counts:   232   265   112    75    12
##    probabilities: 0.333 0.381 0.161 0.108 0.017 
##   left son=1820 (177 obs) right son=1821 (519 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the left,  improve=3.566495, (0 missing)
##       copd              < 0.5    to the left,  improve=2.688595, (0 missing)
##       age               < 70.5   to the right, improve=1.873250, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.737201, (0 missing)
##       reimbursement2008 < 7405   to the left,  improve=1.699902, (0 missing)
## 
## Node number 911: 10 observations
##   predicted class=B3  expected loss=0.4  P(node) =3.63897e-05
##     class counts:     1     1     6     2     0
##    probabilities: 0.100 0.100 0.600 0.200 0.000 
## 
## Node number 912: 58 observations
##   predicted class=B2  expected loss=0.3793103  P(node) =0.0002110603
##     class counts:    20    36     0     1     1
##    probabilities: 0.345 0.621 0.000 0.017 0.017 
## 
## Node number 913: 382 observations,    complexity param=5.313437e-05
##   predicted class=B1  expected loss=0.5890052  P(node) =0.001390087
##     class counts:   157   156    49    19     1
##    probabilities: 0.411 0.408 0.128 0.050 0.003 
##   left son=1826 (25 obs) right son=1827 (357 obs)
##   Primary splits:
##       reimbursement2008 < 3245   to the left,  improve=2.6514540, (0 missing)
##       age               < 80.5   to the right, improve=2.1655330, (0 missing)
##       depression        < 0.5    to the left,  improve=1.2095670, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.1024610, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.7876318, (0 missing)
## 
## Node number 922: 9 observations
##   predicted class=B1  expected loss=0.3333333  P(node) =3.275073e-05
##     class counts:     6     0     1     1     1
##    probabilities: 0.667 0.000 0.111 0.111 0.111 
## 
## Node number 923: 293 observations,    complexity param=7.748763e-05
##   predicted class=B2  expected loss=0.5938567  P(node) =0.001066218
##     class counts:    96   119    48    30     0
##    probabilities: 0.328 0.406 0.164 0.102 0.000 
##   left son=1846 (39 obs) right son=1847 (254 obs)
##   Primary splits:
##       stroke            < 0.5    to the right, improve=2.1766940, (0 missing)
##       age               < 65.5   to the right, improve=1.5636490, (0 missing)
##       reimbursement2008 < 11660  to the right, improve=1.0372370, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.9901623, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.7188410, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 79760  to the right, agree=0.87, adj=0.026, (0 split)
## 
## Node number 924: 220 observations,    complexity param=6.088314e-05
##   predicted class=B1  expected loss=0.65  P(node) =0.0008005735
##     class counts:    77    66    57    16     4
##    probabilities: 0.350 0.300 0.259 0.073 0.018 
##   left son=1848 (132 obs) right son=1849 (88 obs)
##   Primary splits:
##       reimbursement2008 < 12810  to the right, improve=2.5787880, (0 missing)
##       age               < 76.5   to the left,  improve=2.1718510, (0 missing)
##       bucket2008        < 3.5    to the right, improve=1.0384950, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.8392769, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.7969697, (0 missing)
##   Surrogate splits:
##       bucket2008 < 3.5    to the right, agree=0.636, adj=0.091, (0 split)
## 
## Node number 925: 62 observations
##   predicted class=B2  expected loss=0.5322581  P(node) =0.0002256162
##     class counts:    11    29    14     7     1
##    probabilities: 0.177 0.468 0.226 0.113 0.016 
## 
## Node number 944: 76 observations
##   predicted class=B1  expected loss=0.4736842  P(node) =0.0002765618
##     class counts:    40    18    12     5     1
##    probabilities: 0.526 0.237 0.158 0.066 0.013 
## 
## Node number 945: 83 observations
##   predicted class=B2  expected loss=0.6024096  P(node) =0.0003020345
##     class counts:    25    33    21     3     1
##    probabilities: 0.301 0.398 0.253 0.036 0.012 
## 
## Node number 948: 126 observations
##   predicted class=B2  expected loss=0.5079365  P(node) =0.0004585103
##     class counts:    22    62    33     9     0
##    probabilities: 0.175 0.492 0.262 0.071 0.000 
## 
## Node number 949: 111 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.6486486  P(node) =0.0004039257
##     class counts:    13    39    39    17     3
##    probabilities: 0.117 0.351 0.351 0.153 0.027 
##   left son=1898 (54 obs) right son=1899 (57 obs)
##   Primary splits:
##       age               < 75.5   to the left,  improve=1.9702330, (0 missing)
##       reimbursement2008 < 23940  to the left,  improve=1.5183950, (0 missing)
##       stroke            < 0.5    to the right, improve=1.4096100, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=1.1218360, (0 missing)
##       kidney            < 0.5    to the right, improve=0.9749865, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 36125  to the right, agree=0.586, adj=0.148, (0 split)
##       depression        < 0.5    to the right, agree=0.568, adj=0.111, (0 split)
##       alzheimers        < 0.5    to the right, agree=0.550, adj=0.074, (0 split)
##       bucket2008        < 4.5    to the right, agree=0.532, adj=0.037, (0 split)
## 
## Node number 962: 1636 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5177262  P(node) =0.005953356
##     class counts:   789   562   198    80     7
##    probabilities: 0.482 0.344 0.121 0.049 0.004 
##   left son=1924 (1127 obs) right son=1925 (509 obs)
##   Primary splits:
##       alzheimers        < 0.5    to the left,  improve=2.275005, (0 missing)
##       depression        < 0.5    to the left,  improve=2.200834, (0 missing)
##       age               < 56.5   to the right, improve=2.161392, (0 missing)
##       reimbursement2008 < 3635   to the left,  improve=1.571205, (0 missing)
##       copd              < 0.5    to the left,  improve=1.483908, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 9210   to the left,  agree=0.69, adj=0.004, (0 split)
## 
## Node number 963: 578 observations,    complexity param=0.0001439056
##   predicted class=B2  expected loss=0.5916955  P(node) =0.002103325
##     class counts:   220   236    92    27     3
##    probabilities: 0.381 0.408 0.159 0.047 0.005 
##   left son=1926 (339 obs) right son=1927 (239 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=4.643194, (0 missing)
##       copd              < 0.5    to the left,  improve=3.299852, (0 missing)
##       reimbursement2008 < 4535   to the left,  improve=1.771265, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.392171, (0 missing)
##       age               < 39.5   to the right, improve=1.271983, (0 missing)
##   Surrogate splits:
##       age < 43.5   to the right, agree=0.602, adj=0.038, (0 split)
## 
## Node number 964: 1363 observations,    complexity param=6.088314e-05
##   predicted class=B1  expected loss=0.5561262  P(node) =0.004959917
##     class counts:   605   435   219    92    12
##    probabilities: 0.444 0.319 0.161 0.067 0.009 
##   left son=1928 (798 obs) right son=1929 (565 obs)
##   Primary splits:
##       copd              < 0.5    to the left,  improve=7.963265, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=4.055475, (0 missing)
##       reimbursement2008 < 29005  to the left,  improve=3.506334, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.818093, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.815648, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 55265  to the left,  agree=0.589, adj=0.009, (0 split)
##       bucket2008        < 4.5    to the left,  agree=0.589, adj=0.009, (0 split)
##       age               < 27.5   to the right, agree=0.588, adj=0.005, (0 split)
## 
## Node number 965: 4200 observations,    complexity param=0.0004649258
##   predicted class=B2  expected loss=0.597619  P(node) =0.01528368
##     class counts:  1619  1690   634   236    21
##    probabilities: 0.385 0.402 0.151 0.056 0.005 
##   left son=1930 (1953 obs) right son=1931 (2247 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the left,  improve=7.153470, (0 missing)
##       copd              < 0.5    to the left,  improve=3.796544, (0 missing)
##       age               < 49.5   to the left,  improve=2.763159, (0 missing)
##       reimbursement2008 < 3415   to the left,  improve=2.562311, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.867356, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 4495   to the left,  agree=0.563, adj=0.060, (0 split)
##       copd              < 0.5    to the left,  agree=0.551, adj=0.035, (0 split)
##       alzheimers        < 0.5    to the left,  agree=0.540, adj=0.010, (0 split)
##       age               < 45.5   to the left,  agree=0.536, adj=0.002, (0 split)
## 
## Node number 966: 2928 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5529372  P(node) =0.01065491
##     class counts:   904  1309   506   190    19
##    probabilities: 0.309 0.447 0.173 0.065 0.006 
##   left son=1932 (1987 obs) right son=1933 (941 obs)
##   Primary splits:
##       copd              < 0.5    to the left,  improve=5.088653, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=4.205973, (0 missing)
##       reimbursement2008 < 8045   to the left,  improve=3.909055, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.356901, (0 missing)
##       stroke            < 0.5    to the left,  improve=3.071040, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 8235   to the left,  agree=0.68, adj=0.005, (0 split)
## 
## Node number 967: 1464 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.6277322  P(node) =0.005327453
##     class counts:   475   545   290   146     8
##    probabilities: 0.324 0.372 0.198 0.100 0.005 
##   left son=1934 (36 obs) right son=1935 (1428 obs)
##   Primary splits:
##       reimbursement2008 < 8485   to the left,  improve=3.191750, (0 missing)
##       age               < 78.5   to the left,  improve=2.281932, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=2.180745, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.944689, (0 missing)
##       copd              < 0.5    to the left,  improve=1.512341, (0 missing)
## 
## Node number 976: 102 observations
##   predicted class=B1  expected loss=0.4803922  P(node) =0.000371175
##     class counts:    53    28    13     7     1
##    probabilities: 0.520 0.275 0.127 0.069 0.010 
## 
## Node number 977: 961 observations
##   predicted class=B2  expected loss=0.5848075  P(node) =0.003497051
##     class counts:   283   399   179    88    12
##    probabilities: 0.294 0.415 0.186 0.092 0.012 
## 
## Node number 992: 572 observations,    complexity param=0.0001411382
##   predicted class=B1  expected loss=0.5909091  P(node) =0.002081491
##     class counts:   234   183    92    57     6
##    probabilities: 0.409 0.320 0.161 0.100 0.010 
##   left son=1984 (101 obs) right son=1985 (471 obs)
##   Primary splits:
##       reimbursement2008 < 3545   to the left,  improve=4.251847, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=2.618377, (0 missing)
##       age               < 69.5   to the right, improve=2.566628, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.664728, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.532473, (0 missing)
## 
## Node number 993: 392 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.5790816  P(node) =0.001426476
##     class counts:   122   165    75    25     5
##    probabilities: 0.311 0.421 0.191 0.064 0.013 
##   left son=1986 (9 obs) right son=1987 (383 obs)
##   Primary splits:
##       reimbursement2008 < 14460  to the right, improve=2.744913, (0 missing)
##       age               < 48.5   to the left,  improve=1.556717, (0 missing)
##       copd              < 0.5    to the left,  improve=1.522824, (0 missing)
##       bucket2008        < 2.5    to the right, improve=1.319075, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.292043, (0 missing)
## 
## Node number 994: 3172 observations
##   predicted class=B2  expected loss=0.5630517  P(node) =0.01154281
##     class counts:   709  1386   688   337    52
##    probabilities: 0.224 0.437 0.217 0.106 0.016 
## 
## Node number 995: 3650 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.6326027  P(node) =0.01328224
##     class counts:   917  1341   812   510    70
##    probabilities: 0.251 0.367 0.222 0.140 0.019 
##   left son=1990 (2424 obs) right son=1991 (1226 obs)
##   Primary splits:
##       osteoporosis  < 0.5    to the left,  improve=8.187206, (0 missing)
##       depression    < 0.5    to the left,  improve=5.387571, (0 missing)
##       copd          < 0.5    to the left,  improve=5.189054, (0 missing)
##       alzheimers    < 0.5    to the left,  improve=3.710849, (0 missing)
##       heart.failure < 0.5    to the left,  improve=2.971629, (0 missing)
## 
## Node number 1008: 973 observations
##   predicted class=B2  expected loss=0.5611511  P(node) =0.003540718
##     class counts:   160   427   184   174    28
##    probabilities: 0.164 0.439 0.189 0.179 0.029 
## 
## Node number 1009: 318 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5283019  P(node) =0.001157193
##     class counts:    23   150    96    45     4
##    probabilities: 0.072 0.472 0.302 0.142 0.013 
##   left son=2018 (293 obs) right son=2019 (25 obs)
##   Primary splits:
##       reimbursement2008 < 16525  to the right, improve=7.797134, (0 missing)
##       ihd               < 0.5    to the left,  improve=3.194748, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.170376, (0 missing)
##       bucket2008        < 3.5    to the right, improve=1.159119, (0 missing)
##       age               < 55     to the right, improve=1.113448, (0 missing)
## 
## Node number 1014: 1286 observations
##   predicted class=B2  expected loss=0.6251944  P(node) =0.004679716
##     class counts:    74   482   303   348    79
##    probabilities: 0.058 0.375 0.236 0.271 0.061 
## 
## Node number 1015: 251 observations,    complexity param=6.088314e-05
##   predicted class=B4  expected loss=0.6095618  P(node) =0.0009133816
##     class counts:    13    77    50    98    13
##    probabilities: 0.052 0.307 0.199 0.390 0.052 
##   left son=2030 (237 obs) right son=2031 (14 obs)
##   Primary splits:
##       reimbursement2008 < 101585 to the left,  improve=3.425401, (0 missing)
##       age               < 61.5   to the left,  improve=2.440583, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=2.158559, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=2.020557, (0 missing)
##       cancer            < 0.5    to the right, improve=1.778561, (0 missing)
## 
## Node number 1016: 1317 observations,    complexity param=0.0001439056
##   predicted class=B2  expected loss=0.6757783  P(node) =0.004792524
##     class counts:   269   427   234   313    74
##    probabilities: 0.204 0.324 0.178 0.238 0.056 
##   left son=2032 (72 obs) right son=2033 (1245 obs)
##   Primary splits:
##       ihd               < 0.5    to the left,  improve=5.587185, (0 missing)
##       reimbursement2008 < 22435  to the left,  improve=5.039175, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=3.150989, (0 missing)
##       age               < 50.5   to the right, improve=2.709964, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=2.161876, (0 missing)
## 
## Node number 1017: 1172 observations,    complexity param=0.0002036818
##   predicted class=B4  expected loss=0.6953925  P(node) =0.004264873
##     class counts:   272   265   202   357    76
##    probabilities: 0.232 0.226 0.172 0.305 0.065 
##   left son=2034 (191 obs) right son=2035 (981 obs)
##   Primary splits:
##       reimbursement2008 < 43640  to the right, improve=6.105110, (0 missing)
##       bucket2008        < 4.5    to the right, improve=4.295055, (0 missing)
##       ihd               < 0.5    to the left,  improve=2.740224, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=2.395917, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.864237, (0 missing)
##   Surrogate splits:
##       bucket2008 < 4.5    to the right, agree=0.925, adj=0.539, (0 split)
## 
## Node number 1652: 555 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5441441  P(node) =0.002019629
##     class counts:   253   193    76    29     4
##    probabilities: 0.456 0.348 0.137 0.052 0.007 
##   left son=3304 (265 obs) right son=3305 (290 obs)
##   Primary splits:
##       reimbursement2008 < 1955   to the left,  improve=2.2492970, (0 missing)
##       age               < 44.5   to the right, improve=2.2010530, (0 missing)
##       cancer            < 0.5    to the left,  improve=2.0271900, (0 missing)
##       ihd               < 0.5    to the left,  improve=2.0227740, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.6965966, (0 missing)
##   Surrogate splits:
##       ihd    < 0.5    to the left,  agree=0.539, adj=0.034, (0 split)
##       age    < 90.5   to the right, agree=0.532, adj=0.019, (0 split)
##       cancer < 0.5    to the right, agree=0.528, adj=0.011, (0 split)
## 
## Node number 1653: 41 observations
##   predicted class=B2  expected loss=0.4878049  P(node) =0.0001491978
##     class counts:    13    21     1     5     1
##    probabilities: 0.317 0.512 0.024 0.122 0.024 
## 
## Node number 1696: 263 observations
##   predicted class=B1  expected loss=0.4980989  P(node) =0.0009570492
##     class counts:   132    94    26    11     0
##    probabilities: 0.502 0.357 0.099 0.042 0.000 
## 
## Node number 1697: 122 observations
##   predicted class=B2  expected loss=0.5737705  P(node) =0.0004439544
##     class counts:    43    52    13    12     2
##    probabilities: 0.352 0.426 0.107 0.098 0.016 
## 
## Node number 1734: 171 observations
##   predicted class=B1  expected loss=0.4736842  P(node) =0.0006222639
##     class counts:    90    46    24    10     1
##    probabilities: 0.526 0.269 0.140 0.058 0.006 
## 
## Node number 1735: 1057 observations,    complexity param=6.918538e-05
##   predicted class=B1  expected loss=0.5666982  P(node) =0.003846392
##     class counts:   458   410   137    47     5
##    probabilities: 0.433 0.388 0.130 0.044 0.005 
##   left son=3470 (840 obs) right son=3471 (217 obs)
##   Primary splits:
##       age               < 83.5   to the left,  improve=3.809819, (0 missing)
##       kidney            < 0.5    to the left,  improve=2.564065, (0 missing)
##       depression        < 0.5    to the left,  improve=1.351420, (0 missing)
##       copd              < 0.5    to the left,  improve=1.145117, (0 missing)
##       reimbursement2008 < 2975   to the left,  improve=1.026292, (0 missing)
## 
## Node number 1750: 1752 observations,    complexity param=0.0001129105
##   predicted class=B1  expected loss=0.6084475  P(node) =0.006375476
##     class counts:   686   629   294   131    12
##    probabilities: 0.392 0.359 0.168 0.075 0.007 
##   left son=3500 (1099 obs) right son=3501 (653 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=3.008256, (0 missing)
##       age               < 97.5   to the left,  improve=2.167182, (0 missing)
##       reimbursement2008 < 3055   to the left,  improve=1.739250, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.536121, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.306511, (0 missing)
##   Surrogate splits:
##       age < 41.5   to the right, agree=0.628, adj=0.002, (0 split)
## 
## Node number 1751: 39 observations
##   predicted class=B2  expected loss=0.4615385  P(node) =0.0001419198
##     class counts:     6    21     6     6     0
##    probabilities: 0.154 0.538 0.154 0.154 0.000 
## 
## Node number 1752: 112 observations
##   predicted class=B1  expected loss=0.5  P(node) =0.0004075647
##     class counts:    56    33    14     9     0
##    probabilities: 0.500 0.295 0.125 0.080 0.000 
## 
## Node number 1753: 68 observations
##   predicted class=B2  expected loss=0.4852941  P(node) =0.00024745
##     class counts:    22    35     9     2     0
##    probabilities: 0.324 0.515 0.132 0.029 0.000 
## 
## Node number 1754: 403 observations,    complexity param=6.272808e-05
##   predicted class=B1  expected loss=0.6253102  P(node) =0.001466505
##     class counts:   151   147    79    20     6
##    probabilities: 0.375 0.365 0.196 0.050 0.015 
##   left son=3508 (382 obs) right son=3509 (21 obs)
##   Primary splits:
##       reimbursement2008 < 2585   to the left,  improve=1.3835870, (0 missing)
##       age               < 45.5   to the left,  improve=1.1912610, (0 missing)
##       depression        < 0.5    to the left,  improve=0.8974996, (0 missing)
##       cancer            < 0.5    to the right, improve=0.7248908, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.2961750, (0 missing)
## 
## Node number 1755: 30 observations
##   predicted class=B2  expected loss=0.6333333  P(node) =0.0001091691
##     class counts:     5    11     9     5     0
##    probabilities: 0.167 0.367 0.300 0.167 0.000 
## 
## Node number 1766: 336 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5535714  P(node) =0.001222694
##     class counts:   150   128    44    14     0
##    probabilities: 0.446 0.381 0.131 0.042 0.000 
##   left son=3532 (322 obs) right son=3533 (14 obs)
##   Primary splits:
##       age               < 90.5   to the left,  improve=1.4565220, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.1063780, (0 missing)
##       reimbursement2008 < 2325   to the right, improve=0.8683190, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.8476676, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.4098214, (0 missing)
## 
## Node number 1767: 13 observations
##   predicted class=B2  expected loss=0.4615385  P(node) =4.730662e-05
##     class counts:     1     7     2     2     1
##    probabilities: 0.077 0.538 0.154 0.154 0.077 
## 
## Node number 1772: 1057 observations,    complexity param=7.748763e-05
##   predicted class=B1  expected loss=0.589404  P(node) =0.003846392
##     class counts:   434   420   145    54     4
##    probabilities: 0.411 0.397 0.137 0.051 0.004 
##   left son=3544 (1008 obs) right son=3545 (49 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=1.0144070, (0 missing)
##       age               < 46.5   to the left,  improve=1.0088960, (0 missing)
##       reimbursement2008 < 2535   to the right, improve=0.9481247, (0 missing)
##       copd              < 0.5    to the left,  improve=0.6576908, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.5672579, (0 missing)
## 
## Node number 1773: 44 observations
##   predicted class=B2  expected loss=0.4545455  P(node) =0.0001601147
##     class counts:    11    24     5     3     1
##    probabilities: 0.250 0.545 0.114 0.068 0.023 
## 
## Node number 1774: 403 observations
##   predicted class=B2  expected loss=0.5756824  P(node) =0.001466505
##     class counts:   130   171    70    28     4
##    probabilities: 0.323 0.424 0.174 0.069 0.010 
## 
## Node number 1775: 29 observations
##   predicted class=B1  expected loss=0.5517241  P(node) =0.0001055301
##     class counts:    13     7     3     6     0
##    probabilities: 0.448 0.241 0.103 0.207 0.000 
## 
## Node number 1802: 684 observations,    complexity param=5.258089e-05
##   predicted class=B1  expected loss=0.5146199  P(node) =0.002489056
##     class counts:   332   231    95    26     0
##    probabilities: 0.485 0.338 0.139 0.038 0.000 
##   left son=3604 (286 obs) right son=3605 (398 obs)
##   Primary splits:
##       reimbursement2008 < 4365   to the left,  improve=1.4254390, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.1902870, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.8341619, (0 missing)
##       age               < 34     to the left,  improve=0.8175360, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.3590913, (0 missing)
## 
## Node number 1803: 9 observations
##   predicted class=B3  expected loss=0.4444444  P(node) =3.275073e-05
##     class counts:     3     1     5     0     0
##    probabilities: 0.333 0.111 0.556 0.000 0.000 
## 
## Node number 1810: 1608 observations
##   predicted class=B1  expected loss=0.5062189  P(node) =0.005851465
##     class counts:   794   529   193    82    10
##    probabilities: 0.494 0.329 0.120 0.051 0.006 
## 
## Node number 1811: 70 observations
##   predicted class=B2  expected loss=0.4857143  P(node) =0.0002547279
##     class counts:    26    36     6     2     0
##    probabilities: 0.371 0.514 0.086 0.029 0.000 
## 
## Node number 1812: 405 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5012346  P(node) =0.001473783
##     class counts:   202   140    42    18     3
##    probabilities: 0.499 0.346 0.104 0.044 0.007 
##   left son=3624 (329 obs) right son=3625 (76 obs)
##   Primary splits:
##       age               < 83.5   to the left,  improve=1.8474760, (0 missing)
##       reimbursement2008 < 14045  to the left,  improve=1.4437850, (0 missing)
##       kidney            < 0.5    to the right, improve=1.0197570, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.4573240, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.4260458, (0 missing)
## 
## Node number 1813: 452 observations,    complexity param=8.855729e-05
##   predicted class=B1  expected loss=0.579646  P(node) =0.001644815
##     class counts:   190   173    58    30     1
##    probabilities: 0.420 0.383 0.128 0.066 0.002 
##   left son=3626 (362 obs) right son=3627 (90 obs)
##   Primary splits:
##       reimbursement2008 < 3875   to the right, improve=2.645100, (0 missing)
##       age               < 84.5   to the left,  improve=2.429876, (0 missing)
##       bucket2008        < 2.5    to the right, improve=1.612268, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.063100, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.663279, (0 missing)
##   Surrogate splits:
##       age < 32     to the right, agree=0.803, adj=0.011, (0 split)
## 
## Node number 1820: 177 observations
##   predicted class=B1  expected loss=0.559322  P(node) =0.0006440978
##     class counts:    78    62    26    11     0
##    probabilities: 0.441 0.350 0.147 0.062 0.000 
## 
## Node number 1821: 519 observations
##   predicted class=B2  expected loss=0.6088632  P(node) =0.001888626
##     class counts:   154   203    86    64    12
##    probabilities: 0.297 0.391 0.166 0.123 0.023 
## 
## Node number 1826: 25 observations
##   predicted class=B1  expected loss=0.32  P(node) =9.097426e-05
##     class counts:    17     7     1     0     0
##    probabilities: 0.680 0.280 0.040 0.000 0.000 
## 
## Node number 1827: 357 observations,    complexity param=5.313437e-05
##   predicted class=B2  expected loss=0.5826331  P(node) =0.001299112
##     class counts:   140   149    48    19     1
##    probabilities: 0.392 0.417 0.134 0.053 0.003 
##   left son=3654 (91 obs) right son=3655 (266 obs)
##   Primary splits:
##       age               < 80.5   to the right, improve=2.1730570, (0 missing)
##       reimbursement2008 < 4405   to the right, improve=2.0106540, (0 missing)
##       depression        < 0.5    to the left,  improve=0.7793758, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.7738464, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.6298514, (0 missing)
## 
## Node number 1846: 39 observations
##   predicted class=B1  expected loss=0.4871795  P(node) =0.0001419198
##     class counts:    20    12     4     3     0
##    probabilities: 0.513 0.308 0.103 0.077 0.000 
## 
## Node number 1847: 254 observations
##   predicted class=B2  expected loss=0.5787402  P(node) =0.0009242985
##     class counts:    76   107    44    27     0
##    probabilities: 0.299 0.421 0.173 0.106 0.000 
## 
## Node number 1848: 132 observations,    complexity param=6.088314e-05
##   predicted class=B1  expected loss=0.5909091  P(node) =0.0004803441
##     class counts:    54    42    26     9     1
##    probabilities: 0.409 0.318 0.197 0.068 0.008 
##   left son=3696 (105 obs) right son=3697 (27 obs)
##   Primary splits:
##       age               < 84.5   to the left,  improve=4.2569990, (0 missing)
##       reimbursement2008 < 13440  to the left,  improve=1.5425260, (0 missing)
##       ihd               < 0.5    to the right, improve=1.3693110, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.4363743, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.4353832, (0 missing)
## 
## Node number 1849: 88 observations
##   predicted class=B3  expected loss=0.6477273  P(node) =0.0003202294
##     class counts:    23    24    31     7     3
##    probabilities: 0.261 0.273 0.352 0.080 0.034 
## 
## Node number 1898: 54 observations
##   predicted class=B3  expected loss=0.5555556  P(node) =0.0001965044
##     class counts:     8    14    24     7     1
##    probabilities: 0.148 0.259 0.444 0.130 0.019 
## 
## Node number 1899: 57 observations
##   predicted class=B2  expected loss=0.5614035  P(node) =0.0002074213
##     class counts:     5    25    15    10     2
##    probabilities: 0.088 0.439 0.263 0.175 0.035 
## 
## Node number 1924: 1127 observations
##   predicted class=B1  expected loss=0.4960071  P(node) =0.00410112
##     class counts:   568   377   131    47     4
##    probabilities: 0.504 0.335 0.116 0.042 0.004 
## 
## Node number 1925: 509 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5658153  P(node) =0.001852236
##     class counts:   221   185    67    33     3
##    probabilities: 0.434 0.363 0.132 0.065 0.006 
##   left son=3850 (137 obs) right son=3851 (372 obs)
##   Primary splits:
##       reimbursement2008 < 3775   to the left,  improve=1.6880360, (0 missing)
##       depression        < 0.5    to the left,  improve=1.6361880, (0 missing)
##       age               < 96.5   to the left,  improve=1.5026800, (0 missing)
##       copd              < 0.5    to the left,  improve=1.2566690, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.7418596, (0 missing)
## 
## Node number 1926: 339 observations,    complexity param=9.409212e-05
##   predicted class=B1  expected loss=0.5575221  P(node) =0.001233611
##     class counts:   150   127    45    16     1
##    probabilities: 0.442 0.375 0.133 0.047 0.003 
##   left son=3852 (211 obs) right son=3853 (128 obs)
##   Primary splits:
##       reimbursement2008 < 4905   to the left,  improve=1.6963240, (0 missing)
##       age               < 45     to the right, improve=1.4829560, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.2573130, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.6055009, (0 missing)
##       copd              < 0.5    to the left,  improve=0.2708942, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.687, adj=0.172, (0 split)
##       copd       < 0.5    to the left,  agree=0.664, adj=0.109, (0 split)
##       stroke     < 0.5    to the left,  agree=0.652, adj=0.078, (0 split)
## 
## Node number 1927: 239 observations,    complexity param=8.855729e-05
##   predicted class=B2  expected loss=0.5439331  P(node) =0.0008697139
##     class counts:    70   109    47    11     2
##    probabilities: 0.293 0.456 0.197 0.046 0.008 
##   left son=3854 (181 obs) right son=3855 (58 obs)
##   Primary splits:
##       copd              < 0.5    to the left,  improve=6.3468870, (0 missing)
##       reimbursement2008 < 5790   to the right, improve=1.7891020, (0 missing)
##       age               < 60.5   to the left,  improve=1.2691270, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.8740764, (0 missing)
##       stroke            < 0.5    to the right, improve=0.6684821, (0 missing)
##   Surrogate splits:
##       age < 35     to the right, agree=0.762, adj=0.017, (0 split)
## 
## Node number 1928: 798 observations
##   predicted class=B1  expected loss=0.5075188  P(node) =0.002903898
##     class counts:   393   256    95    51     3
##    probabilities: 0.492 0.321 0.119 0.064 0.004 
## 
## Node number 1929: 565 observations,    complexity param=6.088314e-05
##   predicted class=B1  expected loss=0.6247788  P(node) =0.002056018
##     class counts:   212   179   124    41     9
##    probabilities: 0.375 0.317 0.219 0.073 0.016 
##   left son=3858 (116 obs) right son=3859 (449 obs)
##   Primary splits:
##       stroke            < 0.5    to the right, improve=4.0381830, (0 missing)
##       reimbursement2008 < 31655  to the left,  improve=2.7523450, (0 missing)
##       age               < 42.5   to the right, improve=1.7655450, (0 missing)
##       bucket2008        < 4.5    to the left,  improve=1.4692280, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.5615109, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 61780  to the right, agree=0.8, adj=0.026, (0 split)
## 
## Node number 1930: 1953 observations,    complexity param=9.962695e-05
##   predicted class=B1  expected loss=0.578085  P(node) =0.007106909
##     class counts:   824   782   251    88     8
##    probabilities: 0.422 0.400 0.129 0.045 0.004 
##   left son=3860 (343 obs) right son=3861 (1610 obs)
##   Primary splits:
##       reimbursement2008 < 3415   to the left,  improve=3.4037160, (0 missing)
##       age               < 42.5   to the left,  improve=3.2783080, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.6509623, (0 missing)
##       copd              < 0.5    to the left,  improve=0.5598170, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.2946889, (0 missing)
## 
## Node number 1931: 2247 observations,    complexity param=0.0001605101
##   predicted class=B2  expected loss=0.5959057  P(node) =0.008176767
##     class counts:   795   908   383   148    13
##    probabilities: 0.354 0.404 0.170 0.066 0.006 
##   left son=3862 (866 obs) right son=3863 (1381 obs)
##   Primary splits:
##       reimbursement2008 < 5335   to the right, improve=3.344298, (0 missing)
##       copd              < 0.5    to the left,  improve=2.798571, (0 missing)
##       age               < 68.5   to the left,  improve=2.255236, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.653597, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.448247, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.678, adj=0.165, (0 split)
##       age        < 34.5   to the left,  agree=0.616, adj=0.005, (0 split)
## 
## Node number 1932: 1987 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5546049  P(node) =0.007230634
##     class counts:   662   885   320   111     9
##    probabilities: 0.333 0.445 0.161 0.056 0.005 
##   left son=3864 (1964 obs) right son=3865 (23 obs)
##   Primary splits:
##       age               < 98.5   to the left,  improve=3.328502, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.141909, (0 missing)
##       reimbursement2008 < 3085   to the left,  improve=3.126917, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=2.906536, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.332632, (0 missing)
## 
## Node number 1933: 941 observations
##   predicted class=B2  expected loss=0.5494155  P(node) =0.003424271
##     class counts:   242   424   186    79    10
##    probabilities: 0.257 0.451 0.198 0.084 0.011 
## 
## Node number 1934: 36 observations
##   predicted class=B1  expected loss=0.4444444  P(node) =0.0001310029
##     class counts:    20     8     8     0     0
##    probabilities: 0.556 0.222 0.222 0.000 0.000 
## 
## Node number 1935: 1428 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.6239496  P(node) =0.00519645
##     class counts:   455   537   282   146     8
##    probabilities: 0.319 0.376 0.197 0.102 0.006 
##   left son=3870 (837 obs) right son=3871 (591 obs)
##   Primary splits:
##       age               < 78.5   to the left,  improve=2.474561, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=2.118405, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.930317, (0 missing)
##       copd              < 0.5    to the left,  improve=1.447977, (0 missing)
##       reimbursement2008 < 8670   to the right, improve=1.324274, (0 missing)
## 
## Node number 1984: 101 observations
##   predicted class=B2  expected loss=0.5247525  P(node) =0.000367536
##     class counts:    33    48    15     4     1
##    probabilities: 0.327 0.475 0.149 0.040 0.010 
## 
## Node number 1985: 471 observations,    complexity param=5.811572e-05
##   predicted class=B1  expected loss=0.5732484  P(node) =0.001713955
##     class counts:   201   135    77    53     5
##    probabilities: 0.427 0.287 0.163 0.113 0.011 
##   left son=3970 (346 obs) right son=3971 (125 obs)
##   Primary splits:
##       osteoporosis      < 0.5    to the left,  improve=2.506365, (0 missing)
##       reimbursement2008 < 11515  to the left,  improve=2.004779, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.922393, (0 missing)
##       age               < 72.5   to the right, improve=1.840715, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=1.312872, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 3600   to the right, agree=0.737, adj=0.008, (0 split)
## 
## Node number 1986: 9 observations
##   predicted class=B1  expected loss=0.2222222  P(node) =3.275073e-05
##     class counts:     7     2     0     0     0
##    probabilities: 0.778 0.222 0.000 0.000 0.000 
## 
## Node number 1987: 383 observations
##   predicted class=B2  expected loss=0.5744125  P(node) =0.001393726
##     class counts:   115   163    75    25     5
##    probabilities: 0.300 0.426 0.196 0.065 0.013 
## 
## Node number 1990: 2424 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.6575908  P(node) =0.008820864
##     class counts:   663   830   547   335    49
##    probabilities: 0.274 0.342 0.226 0.138 0.020 
##   left son=3980 (1234 obs) right son=3981 (1190 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=3.612677, (0 missing)
##       age               < 67.5   to the right, improve=3.329297, (0 missing)
##       copd              < 0.5    to the left,  improve=3.109296, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=2.737555, (0 missing)
##       reimbursement2008 < 9205   to the right, improve=2.610291, (0 missing)
##   Surrogate splits:
##       alzheimers        < 0.5    to the left,  agree=0.552, adj=0.087, (0 split)
##       copd              < 0.5    to the left,  agree=0.540, adj=0.064, (0 split)
##       age               < 53.5   to the right, agree=0.526, adj=0.035, (0 split)
##       reimbursement2008 < 12525  to the left,  agree=0.522, adj=0.026, (0 split)
##       heart.failure     < 0.5    to the left,  agree=0.520, adj=0.023, (0 split)
## 
## Node number 1991: 1226 observations
##   predicted class=B2  expected loss=0.5831974  P(node) =0.004461378
##     class counts:   254   511   265   175    21
##    probabilities: 0.207 0.417 0.216 0.143 0.017 
## 
## Node number 2018: 293 observations
##   predicted class=B2  expected loss=0.4914676  P(node) =0.001066218
##     class counts:    20   149    81    39     4
##    probabilities: 0.068 0.509 0.276 0.133 0.014 
## 
## Node number 2019: 25 observations
##   predicted class=B3  expected loss=0.4  P(node) =9.097426e-05
##     class counts:     3     1    15     6     0
##    probabilities: 0.120 0.040 0.600 0.240 0.000 
## 
## Node number 2030: 237 observations,    complexity param=6.088314e-05
##   predicted class=B4  expected loss=0.6329114  P(node) =0.000862436
##     class counts:    13    76    49    87    12
##    probabilities: 0.055 0.321 0.207 0.367 0.051 
##   left son=4060 (62 obs) right son=4061 (175 obs)
##   Primary splits:
##       cancer            < 0.5    to the right, improve=2.618202, (0 missing)
##       reimbursement2008 < 90420  to the right, improve=2.488954, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=2.039633, (0 missing)
##       age               < 61.5   to the left,  improve=1.881916, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.753135, (0 missing)
## 
## Node number 2031: 14 observations
##   predicted class=B4  expected loss=0.2142857  P(node) =5.094559e-05
##     class counts:     0     1     1    11     1
##    probabilities: 0.000 0.071 0.071 0.786 0.071 
## 
## Node number 2032: 72 observations
##   predicted class=B1  expected loss=0.5694444  P(node) =0.0002620059
##     class counts:    31    18    11     8     4
##    probabilities: 0.431 0.250 0.153 0.111 0.056 
## 
## Node number 2033: 1245 observations
##   predicted class=B2  expected loss=0.6714859  P(node) =0.004530518
##     class counts:   238   409   223   305    70
##    probabilities: 0.191 0.329 0.179 0.245 0.056 
## 
## Node number 2034: 191 observations,    complexity param=7.748763e-05
##   predicted class=B2  expected loss=0.6753927  P(node) =0.0006950434
##     class counts:    29    62    44    42    14
##    probabilities: 0.152 0.325 0.230 0.220 0.073 
##   left son=4068 (172 obs) right son=4069 (19 obs)
##   Primary splits:
##       age               < 64.5   to the right, improve=2.5420870, (0 missing)
##       reimbursement2008 < 71460  to the left,  improve=2.3514440, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.2597430, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.9221356, (0 missing)
##       ihd               < 0.5    to the right, improve=0.7384918, (0 missing)
## 
## Node number 2035: 981 observations,    complexity param=9.962695e-05
##   predicted class=B4  expected loss=0.6788991  P(node) =0.00356983
##     class counts:   243   203   158   315    62
##    probabilities: 0.248 0.207 0.161 0.321 0.063 
##   left son=4070 (468 obs) right son=4071 (513 obs)
##   Primary splits:
##       reimbursement2008 < 23175  to the left,  improve=5.196818, (0 missing)
##       alzheimers        < 0.5    to the right, improve=3.174409, (0 missing)
##       ihd               < 0.5    to the left,  improve=2.640760, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.689264, (0 missing)
##       age               < 97.5   to the right, improve=1.688586, (0 missing)
##   Surrogate splits:
##       bucket2008    < 3.5    to the left,  agree=0.777, adj=0.532, (0 split)
##       heart.failure < 0.5    to the left,  agree=0.534, adj=0.024, (0 split)
##       age           < 53.5   to the left,  agree=0.531, adj=0.017, (0 split)
##       stroke        < 0.5    to the right, agree=0.525, adj=0.004, (0 split)
## 
## Node number 3304: 265 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.490566  P(node) =0.0009643272
##     class counts:   135    82    34    13     1
##    probabilities: 0.509 0.309 0.128 0.049 0.004 
##   left son=6608 (251 obs) right son=6609 (14 obs)
##   Primary splits:
##       stroke            < 0.5    to the left,  improve=3.807509, (0 missing)
##       ihd               < 0.5    to the left,  improve=2.996787, (0 missing)
##       cancer            < 0.5    to the left,  improve=2.863288, (0 missing)
##       reimbursement2008 < 1815   to the left,  improve=1.417998, (0 missing)
##       depression        < 0.5    to the left,  improve=1.227469, (0 missing)
## 
## Node number 3305: 290 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5931034  P(node) =0.001055301
##     class counts:   118   111    42    16     3
##    probabilities: 0.407 0.383 0.145 0.055 0.010 
##   left son=6610 (213 obs) right son=6611 (77 obs)
##   Primary splits:
##       age               < 81.5   to the left,  improve=1.9355560, (0 missing)
##       reimbursement2008 < 2015   to the right, improve=1.1719950, (0 missing)
##       ihd               < 0.5    to the right, improve=0.8443893, (0 missing)
##       stroke            < 0.5    to the right, improve=0.5640543, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.4757090, (0 missing)
## 
## Node number 3470: 840 observations,    complexity param=6.088314e-05
##   predicted class=B1  expected loss=0.5452381  P(node) =0.003056735
##     class counts:   382   309   103    41     5
##    probabilities: 0.455 0.368 0.123 0.049 0.006 
##   left son=6940 (71 obs) right son=6941 (769 obs)
##   Primary splits:
##       age               < 54.5   to the left,  improve=2.5793890, (0 missing)
##       kidney            < 0.5    to the left,  improve=2.0319410, (0 missing)
##       depression        < 0.5    to the left,  improve=1.3091120, (0 missing)
##       reimbursement2008 < 2945   to the right, improve=1.1530320, (0 missing)
##       copd              < 0.5    to the left,  improve=0.9762638, (0 missing)
## 
## Node number 3471: 217 observations
##   predicted class=B2  expected loss=0.5345622  P(node) =0.0007896566
##     class counts:    76   101    34     6     0
##    probabilities: 0.350 0.465 0.157 0.028 0.000 
## 
## Node number 3500: 1099 observations,    complexity param=9.962695e-05
##   predicted class=B1  expected loss=0.5814377  P(node) =0.003999229
##     class counts:   460   388   168    76     7
##    probabilities: 0.419 0.353 0.153 0.069 0.006 
##   left son=7000 (1074 obs) right son=7001 (25 obs)
##   Primary splits:
##       age               < 95.5   to the left,  improve=2.515661, (0 missing)
##       copd              < 0.5    to the left,  improve=2.359857, (0 missing)
##       cancer            < 0.5    to the left,  improve=1.641148, (0 missing)
##       reimbursement2008 < 2575   to the right, improve=1.347245, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.145174, (0 missing)
## 
## Node number 3501: 653 observations,    complexity param=0.0001129105
##   predicted class=B2  expected loss=0.6309342  P(node) =0.002376248
##     class counts:   226   241   126    55     5
##    probabilities: 0.346 0.369 0.193 0.084 0.008 
##   left son=7002 (303 obs) right son=7003 (350 obs)
##   Primary splits:
##       reimbursement2008 < 2655   to the left,  improve=2.636734, (0 missing)
##       cancer            < 0.5    to the left,  improve=1.461370, (0 missing)
##       age               < 55.5   to the right, improve=1.350106, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.189997, (0 missing)
##       bucket2008        < 1.5    to the left,  improve=1.091246, (0 missing)
##   Surrogate splits:
##       bucket2008 < 1.5    to the left,  agree=0.548, adj=0.026, (0 split)
##       copd       < 0.5    to the right, agree=0.542, adj=0.013, (0 split)
##       age        < 47.5   to the left,  agree=0.539, adj=0.007, (0 split)
## 
## Node number 3508: 382 observations,    complexity param=6.272808e-05
##   predicted class=B2  expected loss=0.6230366  P(node) =0.001390087
##     class counts:   142   144    74    18     4
##    probabilities: 0.372 0.377 0.194 0.047 0.010 
##   left son=7016 (229 obs) right son=7017 (153 obs)
##   Primary splits:
##       depression        < 0.5    to the left,  improve=0.9679873, (0 missing)
##       reimbursement2008 < 2275   to the left,  improve=0.8225283, (0 missing)
##       age               < 75.5   to the right, improve=0.7274055, (0 missing)
##       cancer            < 0.5    to the right, improve=0.6895810, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.4333447, (0 missing)
##   Surrogate splits:
##       alzheimers < 0.5    to the left,  agree=0.620, adj=0.052, (0 split)
##       age        < 50.5   to the right, agree=0.613, adj=0.033, (0 split)
## 
## Node number 3509: 21 observations
##   predicted class=B1  expected loss=0.5714286  P(node) =7.641838e-05
##     class counts:     9     3     5     2     2
##    probabilities: 0.429 0.143 0.238 0.095 0.095 
## 
## Node number 3532: 322 observations
##   predicted class=B1  expected loss=0.5465839  P(node) =0.001171748
##     class counts:   146   119    43    14     0
##    probabilities: 0.453 0.370 0.134 0.043 0.000 
## 
## Node number 3533: 14 observations
##   predicted class=B2  expected loss=0.3571429  P(node) =5.094559e-05
##     class counts:     4     9     1     0     0
##    probabilities: 0.286 0.643 0.071 0.000 0.000 
## 
## Node number 3544: 1008 observations,    complexity param=7.748763e-05
##   predicted class=B1  expected loss=0.5853175  P(node) =0.003668082
##     class counts:   418   400   133    53     4
##    probabilities: 0.415 0.397 0.132 0.053 0.004 
##   left son=7088 (275 obs) right son=7089 (733 obs)
##   Primary splits:
##       reimbursement2008 < 2535   to the right, improve=0.9732083, (0 missing)
##       age               < 39     to the left,  improve=0.9699606, (0 missing)
##       copd              < 0.5    to the left,  improve=0.8468269, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.4615681, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.4416739, (0 missing)
##   Surrogate splits:
##       age < 36.5   to the left,  agree=0.728, adj=0.004, (0 split)
## 
## Node number 3545: 49 observations
##   predicted class=B2  expected loss=0.5918367  P(node) =0.0001783096
##     class counts:    16    20    12     1     0
##    probabilities: 0.327 0.408 0.245 0.020 0.000 
## 
## Node number 3604: 286 observations
##   predicted class=B1  expected loss=0.4685315  P(node) =0.001040746
##     class counts:   152    94    35     5     0
##    probabilities: 0.531 0.329 0.122 0.017 0.000 
## 
## Node number 3605: 398 observations,    complexity param=5.258089e-05
##   predicted class=B1  expected loss=0.5477387  P(node) =0.00144831
##     class counts:   180   137    60    21     0
##    probabilities: 0.452 0.344 0.151 0.053 0.000 
##   left son=7210 (340 obs) right son=7211 (58 obs)
##   Primary splits:
##       reimbursement2008 < 4700   to the right, improve=5.7797840, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.1372610, (0 missing)
##       age               < 34.5   to the left,  improve=0.9964329, (0 missing)
##       bucket2008        < 2.5    to the right, improve=0.5848011, (0 missing)
##       kidney            < 0.5    to the right, improve=0.4151452, (0 missing)
## 
## Node number 3624: 329 observations
##   predicted class=B1  expected loss=0.4832827  P(node) =0.001197221
##     class counts:   170   105    35    16     3
##    probabilities: 0.517 0.319 0.106 0.049 0.009 
## 
## Node number 3625: 76 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.5394737  P(node) =0.0002765618
##     class counts:    32    35     7     2     0
##    probabilities: 0.421 0.461 0.092 0.026 0.000 
##   left son=7250 (21 obs) right son=7251 (55 obs)
##   Primary splits:
##       reimbursement2008 < 6785   to the right, improve=3.2066300, (0 missing)
##       bucket2008        < 2.5    to the right, improve=3.1159910, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.9967220, (0 missing)
##       age               < 85.5   to the right, improve=1.1176690, (0 missing)
##       kidney            < 0.5    to the right, improve=0.9258269, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.921, adj=0.714, (0 split)
##       kidney     < 0.5    to the right, agree=0.789, adj=0.238, (0 split)
## 
## Node number 3626: 362 observations
##   predicted class=B1  expected loss=0.5552486  P(node) =0.001317307
##     class counts:   161   128    47    25     1
##    probabilities: 0.445 0.354 0.130 0.069 0.003 
## 
## Node number 3627: 90 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.5  P(node) =0.0003275073
##     class counts:    29    45    11     5     0
##    probabilities: 0.322 0.500 0.122 0.056 0.000 
##   left son=7254 (21 obs) right son=7255 (69 obs)
##   Primary splits:
##       age               < 69.5   to the left,  improve=3.1544510, (0 missing)
##       alzheimers        < 0.5    to the right, improve=3.1535260, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.7000000, (0 missing)
##       reimbursement2008 < 3185   to the left,  improve=1.5133190, (0 missing)
##       copd              < 0.5    to the right, improve=0.3083333, (0 missing)
## 
## Node number 3654: 91 observations
##   predicted class=B1  expected loss=0.5054945  P(node) =0.0003311463
##     class counts:    45    31     9     6     0
##    probabilities: 0.495 0.341 0.099 0.066 0.000 
## 
## Node number 3655: 266 observations
##   predicted class=B2  expected loss=0.556391  P(node) =0.0009679661
##     class counts:    95   118    39    13     1
##    probabilities: 0.357 0.444 0.147 0.049 0.004 
## 
## Node number 3696: 105 observations
##   predicted class=B1  expected loss=0.5238095  P(node) =0.0003820919
##     class counts:    50    35    16     3     1
##    probabilities: 0.476 0.333 0.152 0.029 0.010 
## 
## Node number 3697: 27 observations
##   predicted class=B3  expected loss=0.6296296  P(node) =9.82522e-05
##     class counts:     4     7    10     6     0
##    probabilities: 0.148 0.259 0.370 0.222 0.000 
## 
## Node number 3850: 137 observations
##   predicted class=B1  expected loss=0.4963504  P(node) =0.000498539
##     class counts:    69    41    17     9     1
##    probabilities: 0.504 0.299 0.124 0.066 0.007 
## 
## Node number 3851: 372 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5913978  P(node) =0.001353697
##     class counts:   152   144    50    24     2
##    probabilities: 0.409 0.387 0.134 0.065 0.005 
##   left son=7702 (330 obs) right son=7703 (42 obs)
##   Primary splits:
##       reimbursement2008 < 4055   to the right, improve=3.5107950, (0 missing)
##       age               < 96     to the left,  improve=2.0766610, (0 missing)
##       depression        < 0.5    to the left,  improve=1.3295540, (0 missing)
##       copd              < 0.5    to the left,  improve=1.2612770, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.1950857, (0 missing)
## 
## Node number 3852: 211 observations,    complexity param=9.409212e-05
##   predicted class=B1  expected loss=0.563981  P(node) =0.0007678228
##     class counts:    92    89    23     7     0
##    probabilities: 0.436 0.422 0.109 0.033 0.000 
##   left son=7704 (142 obs) right son=7705 (69 obs)
##   Primary splits:
##       reimbursement2008 < 4075   to the left,  improve=3.4884480, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.9268848, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.8762896, (0 missing)
##       age               < 95     to the right, improve=0.6396384, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.3979516, (0 missing)
##   Surrogate splits:
##       age < 96.5   to the left,  agree=0.687, adj=0.043, (0 split)
## 
## Node number 3853: 128 observations
##   predicted class=B1  expected loss=0.546875  P(node) =0.0004657882
##     class counts:    58    38    22     9     1
##    probabilities: 0.453 0.297 0.172 0.070 0.008 
## 
## Node number 3854: 181 observations
##   predicted class=B2  expected loss=0.4861878  P(node) =0.0006586537
##     class counts:    56    93    23     7     2
##    probabilities: 0.309 0.514 0.127 0.039 0.011 
## 
## Node number 3855: 58 observations
##   predicted class=B3  expected loss=0.5862069  P(node) =0.0002110603
##     class counts:    14    16    24     4     0
##    probabilities: 0.241 0.276 0.414 0.069 0.000 
## 
## Node number 3858: 116 observations,    complexity param=6.088314e-05
##   predicted class=B2  expected loss=0.5517241  P(node) =0.0004221206
##     class counts:    41    52    14     7     2
##    probabilities: 0.353 0.448 0.121 0.060 0.017 
##   left son=7716 (63 obs) right son=7717 (53 obs)
##   Primary splits:
##       age               < 74.5   to the right, improve=2.8010500, (0 missing)
##       reimbursement2008 < 17265  to the left,  improve=2.4722090, (0 missing)
##       bucket2008        < 4.5    to the left,  improve=1.9776500, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.5892240, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.5845524, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 15590  to the right, agree=0.603, adj=0.132, (0 split)
##       heart.failure     < 0.5    to the right, agree=0.578, adj=0.075, (0 split)
##       alzheimers        < 0.5    to the right, agree=0.560, adj=0.038, (0 split)
##       osteoporosis      < 0.5    to the left,  agree=0.552, adj=0.019, (0 split)
##       bucket2008        < 3.5    to the right, agree=0.552, adj=0.019, (0 split)
## 
## Node number 3859: 449 observations
##   predicted class=B1  expected loss=0.6191537  P(node) =0.001633898
##     class counts:   171   127   110    34     7
##    probabilities: 0.381 0.283 0.245 0.076 0.016 
## 
## Node number 3860: 343 observations
##   predicted class=B1  expected loss=0.5014577  P(node) =0.001248167
##     class counts:   171   126    33    11     2
##    probabilities: 0.499 0.367 0.096 0.032 0.006 
## 
## Node number 3861: 1610 observations,    complexity param=9.962695e-05
##   predicted class=B2  expected loss=0.5925466  P(node) =0.005858742
##     class counts:   653   656   218    77     6
##    probabilities: 0.406 0.407 0.135 0.048 0.004 
##   left son=7722 (43 obs) right son=7723 (1567 obs)
##   Primary splits:
##       age               < 42.5   to the left,  improve=2.9787580, (0 missing)
##       reimbursement2008 < 3475   to the right, improve=1.6291410, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.4315089, (0 missing)
##       copd              < 0.5    to the left,  improve=0.2703192, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.2492479, (0 missing)
## 
## Node number 3862: 866 observations,    complexity param=0.0001605101
##   predicted class=B1  expected loss=0.6120092  P(node) =0.003151348
##     class counts:   336   322   139    64     5
##    probabilities: 0.388 0.372 0.161 0.074 0.006 
##   left son=7724 (129 obs) right son=7725 (737 obs)
##   Primary splits:
##       reimbursement2008 < 8115   to the right, improve=2.0175360, (0 missing)
##       age               < 89.5   to the left,  improve=1.8274460, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.6689370, (0 missing)
##       bucket2008        < 2.5    to the right, improve=1.3462440, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.5970317, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.984, adj=0.891, (0 split)
## 
## Node number 3863: 1381 observations,    complexity param=7.19528e-05
##   predicted class=B2  expected loss=0.5756698  P(node) =0.005025418
##     class counts:   459   586   244    84     8
##    probabilities: 0.332 0.424 0.177 0.061 0.006 
##   left son=7726 (997 obs) right son=7727 (384 obs)
##   Primary splits:
##       copd              < 0.5    to the left,  improve=3.5627620, (0 missing)
##       age               < 37.5   to the right, improve=2.2016010, (0 missing)
##       reimbursement2008 < 5195   to the left,  improve=1.9417980, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.9283600, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.3841814, (0 missing)
##   Surrogate splits:
##       age               < 34     to the right, agree=0.723, adj=0.005, (0 split)
##       reimbursement2008 < 5325   to the left,  agree=0.723, adj=0.005, (0 split)
## 
## Node number 3864: 1964 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.552444  P(node) =0.007146938
##     class counts:   656   879   309   111     9
##    probabilities: 0.334 0.448 0.157 0.057 0.005 
##   left son=7728 (22 obs) right son=7729 (1942 obs)
##   Primary splits:
##       reimbursement2008 < 3085   to the left,  improve=3.418849, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=3.308540, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=2.919418, (0 missing)
##       age               < 66.5   to the right, improve=1.961336, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.448295, (0 missing)
## 
## Node number 3865: 23 observations
##   predicted class=B3  expected loss=0.5217391  P(node) =8.369632e-05
##     class counts:     6     6    11     0     0
##    probabilities: 0.261 0.261 0.478 0.000 0.000 
## 
## Node number 3870: 837 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.6356033  P(node) =0.003045818
##     class counts:   292   305   155    82     3
##    probabilities: 0.349 0.364 0.185 0.098 0.004 
##   left son=7740 (639 obs) right son=7741 (198 obs)
##   Primary splits:
##       reimbursement2008 < 21320  to the left,  improve=1.8992410, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.6748470, (0 missing)
##       age               < 49.5   to the left,  improve=1.4981360, (0 missing)
##       bucket2008        < 3.5    to the left,  improve=1.4460210, (0 missing)
##       stroke            < 0.5    to the right, improve=0.7571134, (0 missing)
##   Surrogate splits:
##       bucket2008 < 3.5    to the left,  agree=0.955, adj=0.808, (0 split)
## 
## Node number 3871: 591 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.607445  P(node) =0.002150632
##     class counts:   163   232   127    64     5
##    probabilities: 0.276 0.393 0.215 0.108 0.008 
##   left son=7742 (122 obs) right son=7743 (469 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the left,  improve=3.4607800, (0 missing)
##       reimbursement2008 < 8775   to the left,  improve=1.9199300, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.2822570, (0 missing)
##       copd              < 0.5    to the left,  improve=1.1325150, (0 missing)
##       age               < 80.5   to the right, improve=0.6677683, (0 missing)
## 
## Node number 3970: 346 observations
##   predicted class=B1  expected loss=0.5375723  P(node) =0.001259084
##     class counts:   160    92    52    37     5
##    probabilities: 0.462 0.266 0.150 0.107 0.014 
## 
## Node number 3971: 125 observations,    complexity param=5.811572e-05
##   predicted class=B2  expected loss=0.656  P(node) =0.0004548713
##     class counts:    41    43    25    16     0
##    probabilities: 0.328 0.344 0.200 0.128 0.000 
##   left son=7942 (106 obs) right son=7943 (19 obs)
##   Primary splits:
##       age               < 62     to the right, improve=3.3415930, (0 missing)
##       reimbursement2008 < 11475  to the left,  improve=2.2730020, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.7920000, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.7319750, (0 missing)
##       stroke            < 0.5    to the right, improve=0.5402967, (0 missing)
## 
## Node number 3980: 1234 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.6612642  P(node) =0.00449049
##     class counts:   374   418   252   160    30
##    probabilities: 0.303 0.339 0.204 0.130 0.024 
##   left son=7960 (349 obs) right son=7961 (885 obs)
##   Primary splits:
##       reimbursement2008 < 12135  to the right, improve=3.745241, (0 missing)
##       age               < 67.5   to the left,  improve=3.421516, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.338981, (0 missing)
##       bucket2008        < 2.5    to the right, improve=1.254047, (0 missing)
##       copd              < 0.5    to the right, improve=1.093433, (0 missing)
## 
## Node number 3981: 1190 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.6537815  P(node) =0.004330375
##     class counts:   289   412   295   175    19
##    probabilities: 0.243 0.346 0.248 0.147 0.016 
##   left son=7962 (547 obs) right son=7963 (643 obs)
##   Primary splits:
##       copd              < 0.5    to the left,  improve=2.655589, (0 missing)
##       age               < 82.5   to the right, improve=1.853724, (0 missing)
##       stroke            < 0.5    to the right, improve=1.583996, (0 missing)
##       reimbursement2008 < 6355   to the right, improve=1.325969, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.085887, (0 missing)
##   Surrogate splits:
##       heart.failure     < 0.5    to the left,  agree=0.562, adj=0.048, (0 split)
##       reimbursement2008 < 7315   to the left,  agree=0.555, adj=0.031, (0 split)
##       alzheimers        < 0.5    to the left,  agree=0.543, adj=0.005, (0 split)
##       age               < 28.5   to the left,  agree=0.542, adj=0.004, (0 split)
## 
## Node number 4060: 62 observations
##   predicted class=B2  expected loss=0.5806452  P(node) =0.0002256162
##     class counts:     3    26    17    15     1
##    probabilities: 0.048 0.419 0.274 0.242 0.016 
## 
## Node number 4061: 175 observations
##   predicted class=B4  expected loss=0.5885714  P(node) =0.0006368198
##     class counts:    10    50    32    72    11
##    probabilities: 0.057 0.286 0.183 0.411 0.063 
## 
## Node number 4068: 172 observations
##   predicted class=B2  expected loss=0.6511628  P(node) =0.0006259029
##     class counts:    27    60    35    39    11
##    probabilities: 0.157 0.349 0.203 0.227 0.064 
## 
## Node number 4069: 19 observations
##   predicted class=B3  expected loss=0.5263158  P(node) =6.914044e-05
##     class counts:     2     2     9     3     3
##    probabilities: 0.105 0.105 0.474 0.158 0.158 
## 
## Node number 4070: 468 observations,    complexity param=7.379774e-05
##   predicted class=B1  expected loss=0.7200855  P(node) =0.001703038
##     class counts:   131   112    77   122    26
##    probabilities: 0.280 0.239 0.165 0.261 0.056 
##   left son=8140 (457 obs) right son=8141 (11 obs)
##   Primary splits:
##       age               < 93.5   to the left,  improve=1.955850, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=1.902288, (0 missing)
##       reimbursement2008 < 19700  to the right, improve=1.873153, (0 missing)
##       ihd               < 0.5    to the right, improve=1.868954, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.716285, (0 missing)
## 
## Node number 4071: 513 observations
##   predicted class=B4  expected loss=0.6237817  P(node) =0.001866792
##     class counts:   112    91    81   193    36
##    probabilities: 0.218 0.177 0.158 0.376 0.070 
## 
## Node number 6608: 251 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.4701195  P(node) =0.0009133816
##     class counts:   133    73    33    11     1
##    probabilities: 0.530 0.291 0.131 0.044 0.004 
##   left son=13216 (235 obs) right son=13217 (16 obs)
##   Primary splits:
##       cancer            < 0.5    to the left,  improve=2.6866090, (0 missing)
##       ihd               < 0.5    to the left,  improve=2.4584220, (0 missing)
##       reimbursement2008 < 1815   to the left,  improve=1.2636760, (0 missing)
##       age               < 60.5   to the right, improve=1.0616730, (0 missing)
##       depression        < 0.5    to the left,  improve=0.7871996, (0 missing)
## 
## Node number 6609: 14 observations
##   predicted class=B2  expected loss=0.3571429  P(node) =5.094559e-05
##     class counts:     2     9     1     2     0
##    probabilities: 0.143 0.643 0.071 0.143 0.000 
## 
## Node number 6610: 213 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5633803  P(node) =0.0007751007
##     class counts:    93    74    30    15     1
##    probabilities: 0.437 0.347 0.141 0.070 0.005 
##   left son=13220 (201 obs) right son=13221 (12 obs)
##   Primary splits:
##       age               < 44.5   to the right, improve=1.7572700, (0 missing)
##       reimbursement2008 < 2005   to the right, improve=1.0657280, (0 missing)
##       cancer            < 0.5    to the right, improve=0.3892571, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.3640621, (0 missing)
##       depression        < 0.5    to the right, improve=0.3467310, (0 missing)
## 
## Node number 6611: 77 observations
##   predicted class=B2  expected loss=0.5194805  P(node) =0.0002802007
##     class counts:    25    37    12     1     2
##    probabilities: 0.325 0.481 0.156 0.013 0.026 
## 
## Node number 6940: 71 observations
##   predicted class=B1  expected loss=0.4366197  P(node) =0.0002583669
##     class counts:    40    16    11     3     1
##    probabilities: 0.563 0.225 0.155 0.042 0.014 
## 
## Node number 6941: 769 observations,    complexity param=6.088314e-05
##   predicted class=B1  expected loss=0.5552666  P(node) =0.002798368
##     class counts:   342   293    92    38     4
##    probabilities: 0.445 0.381 0.120 0.049 0.005 
##   left son=13882 (472 obs) right son=13883 (297 obs)
##   Primary splits:
##       age               < 70.5   to the right, improve=2.5248320, (0 missing)
##       depression        < 0.5    to the left,  improve=1.8557100, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.7236880, (0 missing)
##       reimbursement2008 < 2665   to the right, improve=1.1252400, (0 missing)
##       copd              < 0.5    to the left,  improve=0.9387137, (0 missing)
## 
## Node number 7000: 1074 observations,    complexity param=7.19528e-05
##   predicted class=B1  expected loss=0.5772812  P(node) =0.003908254
##     class counts:   454   373   166    74     7
##    probabilities: 0.423 0.347 0.155 0.069 0.007 
##   left son=14000 (808 obs) right son=14001 (266 obs)
##   Primary splits:
##       copd              < 0.5    to the left,  improve=2.7468870, (0 missing)
##       cancer            < 0.5    to the left,  improve=1.8315690, (0 missing)
##       age               < 78.5   to the left,  improve=1.6074350, (0 missing)
##       reimbursement2008 < 2575   to the right, improve=1.2651380, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.9951466, (0 missing)
## 
## Node number 7001: 25 observations
##   predicted class=B2  expected loss=0.4  P(node) =9.097426e-05
##     class counts:     6    15     2     2     0
##    probabilities: 0.240 0.600 0.080 0.080 0.000 
## 
## Node number 7002: 303 observations
##   predicted class=B1  expected loss=0.6039604  P(node) =0.001102608
##     class counts:   120    99    62    21     1
##    probabilities: 0.396 0.327 0.205 0.069 0.003 
## 
## Node number 7003: 350 observations
##   predicted class=B2  expected loss=0.5942857  P(node) =0.00127364
##     class counts:   106   142    64    34     4
##    probabilities: 0.303 0.406 0.183 0.097 0.011 
## 
## Node number 7016: 229 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5938865  P(node) =0.0008333242
##     class counts:    93    82    44     8     2
##    probabilities: 0.406 0.358 0.192 0.035 0.009 
##   left son=14032 (15 obs) right son=14033 (214 obs)
##   Primary splits:
##       cancer            < 0.5    to the right, improve=1.9222650, (0 missing)
##       reimbursement2008 < 2515   to the left,  improve=1.4164780, (0 missing)
##       age               < 94     to the left,  improve=1.2547820, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.6631197, (0 missing)
##       copd              < 0.5    to the right, improve=0.2469242, (0 missing)
## 
## Node number 7017: 153 observations,    complexity param=6.272808e-05
##   predicted class=B2  expected loss=0.5947712  P(node) =0.0005567625
##     class counts:    49    62    30    10     2
##    probabilities: 0.320 0.405 0.196 0.065 0.013 
##   left son=14034 (14 obs) right son=14035 (139 obs)
##   Primary splits:
##       reimbursement2008 < 2545   to the right, improve=2.7113570, (0 missing)
##       age               < 45     to the left,  improve=1.6972360, (0 missing)
##       cancer            < 0.5    to the left,  improve=0.6348039, (0 missing)
##       copd              < 0.5    to the right, improve=0.3887797, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.2839287, (0 missing)
## 
## Node number 7088: 275 observations,    complexity param=7.748763e-05
##   predicted class=B2  expected loss=0.56  P(node) =0.001000717
##     class counts:   108   121    34    11     1
##    probabilities: 0.393 0.440 0.124 0.040 0.004 
##   left son=14176 (44 obs) right son=14177 (231 obs)
##   Primary splits:
##       age               < 63.5   to the left,  improve=2.2068400, (0 missing)
##       reimbursement2008 < 2555   to the right, improve=1.7374730, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.5968660, (0 missing)
##       copd              < 0.5    to the left,  improve=0.9046397, (0 missing)
##       heart.failure     < 0.5    to the right, improve=0.5279104, (0 missing)
## 
## Node number 7089: 733 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5770805  P(node) =0.002667365
##     class counts:   310   279    99    42     3
##    probabilities: 0.423 0.381 0.135 0.057 0.004 
##   left son=14178 (10 obs) right son=14179 (723 obs)
##   Primary splits:
##       age               < 97.5   to the right, improve=1.1550490, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.1107930, (0 missing)
##       reimbursement2008 < 2495   to the right, improve=0.7495829, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.7242328, (0 missing)
##       depression        < 0.5    to the left,  improve=0.5684301, (0 missing)
## 
## Node number 7210: 340 observations
##   predicted class=B1  expected loss=0.5088235  P(node) =0.00123725
##     class counts:   167   107    48    18     0
##    probabilities: 0.491 0.315 0.141 0.053 0.000 
## 
## Node number 7211: 58 observations
##   predicted class=B2  expected loss=0.4827586  P(node) =0.0002110603
##     class counts:    13    30    12     3     0
##    probabilities: 0.224 0.517 0.207 0.052 0.000 
## 
## Node number 7250: 21 observations
##   predicted class=B1  expected loss=0.3333333  P(node) =7.641838e-05
##     class counts:    14     5     2     0     0
##    probabilities: 0.667 0.238 0.095 0.000 0.000 
## 
## Node number 7251: 55 observations
##   predicted class=B2  expected loss=0.4545455  P(node) =0.0002001434
##     class counts:    18    30     5     2     0
##    probabilities: 0.327 0.545 0.091 0.036 0.000 
## 
## Node number 7254: 21 observations
##   predicted class=B1  expected loss=0.4285714  P(node) =7.641838e-05
##     class counts:    12     6     1     2     0
##    probabilities: 0.571 0.286 0.048 0.095 0.000 
## 
## Node number 7255: 69 observations
##   predicted class=B2  expected loss=0.4347826  P(node) =0.000251089
##     class counts:    17    39    10     3     0
##    probabilities: 0.246 0.565 0.145 0.043 0.000 
## 
## Node number 7702: 330 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.569697  P(node) =0.00120086
##     class counts:   142   119    44    23     2
##    probabilities: 0.430 0.361 0.133 0.070 0.006 
##   left son=15404 (309 obs) right son=15405 (21 obs)
##   Primary splits:
##       reimbursement2008 < 4185   to the right, improve=2.2681710, (0 missing)
##       age               < 96     to the left,  improve=2.1333520, (0 missing)
##       depression        < 0.5    to the left,  improve=0.7533962, (0 missing)
##       copd              < 0.5    to the left,  improve=0.6700147, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.3769465, (0 missing)
## 
## Node number 7703: 42 observations
##   predicted class=B2  expected loss=0.4047619  P(node) =0.0001528368
##     class counts:    10    25     6     1     0
##    probabilities: 0.238 0.595 0.143 0.024 0.000 
## 
## Node number 7704: 142 observations
##   predicted class=B1  expected loss=0.5  P(node) =0.0005167338
##     class counts:    71    51    15     5     0
##    probabilities: 0.500 0.359 0.106 0.035 0.000 
## 
## Node number 7705: 69 observations
##   predicted class=B2  expected loss=0.4492754  P(node) =0.000251089
##     class counts:    21    38     8     2     0
##    probabilities: 0.304 0.551 0.116 0.029 0.000 
## 
## Node number 7716: 63 observations
##   predicted class=B1  expected loss=0.5714286  P(node) =0.0002292551
##     class counts:    27    21    10     4     1
##    probabilities: 0.429 0.333 0.159 0.063 0.016 
## 
## Node number 7717: 53 observations
##   predicted class=B2  expected loss=0.4150943  P(node) =0.0001928654
##     class counts:    14    31     4     3     1
##    probabilities: 0.264 0.585 0.075 0.057 0.019 
## 
## Node number 7722: 43 observations
##   predicted class=B1  expected loss=0.3953488  P(node) =0.0001564757
##     class counts:    26    11     3     3     0
##    probabilities: 0.605 0.256 0.070 0.070 0.000 
## 
## Node number 7723: 1567 observations,    complexity param=9.962695e-05
##   predicted class=B2  expected loss=0.5883854  P(node) =0.005702267
##     class counts:   627   645   215    74     6
##    probabilities: 0.400 0.412 0.137 0.047 0.004 
##   left son=15446 (1527 obs) right son=15447 (40 obs)
##   Primary splits:
##       age               < 50.5   to the right, improve=1.7032880, (0 missing)
##       reimbursement2008 < 3475   to the right, improve=1.5459000, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.4552758, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.2471234, (0 missing)
##       copd              < 0.5    to the left,  improve=0.2014160, (0 missing)
## 
## Node number 7724: 129 observations
##   predicted class=B2  expected loss=0.5271318  P(node) =0.0004694272
##     class counts:    46    61    16     6     0
##    probabilities: 0.357 0.473 0.124 0.047 0.000 
## 
## Node number 7725: 737 observations,    complexity param=9.962695e-05
##   predicted class=B1  expected loss=0.6065129  P(node) =0.002681921
##     class counts:   290   261   123    58     5
##    probabilities: 0.393 0.354 0.167 0.079 0.007 
##   left son=15450 (703 obs) right son=15451 (34 obs)
##   Primary splits:
##       age               < 94.5   to the left,  improve=1.9050170, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.5567680, (0 missing)
##       reimbursement2008 < 6575   to the right, improve=1.5078350, (0 missing)
##       copd              < 0.5    to the left,  improve=0.5423379, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.5213862, (0 missing)
## 
## Node number 7726: 997 observations,    complexity param=7.19528e-05
##   predicted class=B2  expected loss=0.5927783  P(node) =0.003628054
##     class counts:   357   406   171    57     6
##    probabilities: 0.358 0.407 0.172 0.057 0.006 
##   left son=15452 (297 obs) right son=15453 (700 obs)
##   Primary splits:
##       age               < 69.5   to the left,  improve=2.4458440, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=2.2624190, (0 missing)
##       reimbursement2008 < 4135   to the right, improve=1.8635870, (0 missing)
##       stroke            < 0.5    to the right, improve=0.3191114, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.3114115, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 5325   to the right, agree=0.703, adj=0.003, (0 split)
## 
## Node number 7727: 384 observations
##   predicted class=B2  expected loss=0.53125  P(node) =0.001397365
##     class counts:   102   180    73    27     2
##    probabilities: 0.266 0.469 0.190 0.070 0.005 
## 
## Node number 7728: 22 observations
##   predicted class=B1  expected loss=0.3636364  P(node) =8.005735e-05
##     class counts:    14     5     1     2     0
##    probabilities: 0.636 0.227 0.045 0.091 0.000 
## 
## Node number 7729: 1942 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5499485  P(node) =0.007066881
##     class counts:   642   874   308   109     9
##    probabilities: 0.331 0.450 0.159 0.056 0.005 
##   left son=15458 (889 obs) right son=15459 (1053 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the left,  improve=3.215424, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=2.693064, (0 missing)
##       reimbursement2008 < 8025   to the left,  improve=2.054172, (0 missing)
##       age               < 66.5   to the right, improve=1.953237, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.238773, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 3815   to the left,  agree=0.545, adj=0.007, (0 split)
##       age               < 31.5   to the left,  agree=0.544, adj=0.003, (0 split)
## 
## Node number 7740: 639 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.6353678  P(node) =0.002325302
##     class counts:   233   221   124    59     2
##    probabilities: 0.365 0.346 0.194 0.092 0.003 
##   left son=15480 (83 obs) right son=15481 (556 obs)
##   Primary splits:
##       age               < 49.5   to the left,  improve=1.7453130, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.0790130, (0 missing)
##       reimbursement2008 < 10445  to the right, improve=1.0644190, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.9731198, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.6917546, (0 missing)
## 
## Node number 7741: 198 observations
##   predicted class=B2  expected loss=0.5757576  P(node) =0.0007205162
##     class counts:    59    84    31    23     1
##    probabilities: 0.298 0.424 0.157 0.116 0.005 
## 
## Node number 7742: 122 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5983607  P(node) =0.0004439544
##     class counts:    49    39    22    12     0
##    probabilities: 0.402 0.320 0.180 0.098 0.000 
##   left son=15484 (40 obs) right son=15485 (82 obs)
##   Primary splits:
##       reimbursement2008 < 11560  to the left,  improve=2.7817470, (0 missing)
##       age               < 80.5   to the right, improve=1.9103730, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.4632510, (0 missing)
##       copd              < 0.5    to the left,  improve=1.0641520, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.9890302, (0 missing)
## 
## Node number 7743: 469 observations
##   predicted class=B2  expected loss=0.5884861  P(node) =0.001706677
##     class counts:   114   193   105    52     5
##    probabilities: 0.243 0.412 0.224 0.111 0.011 
## 
## Node number 7942: 106 observations,    complexity param=5.811572e-05
##   predicted class=B1  expected loss=0.6320755  P(node) =0.0003857309
##     class counts:    39    39    16    12     0
##    probabilities: 0.368 0.368 0.151 0.113 0.000 
##   left son=15884 (93 obs) right son=15885 (13 obs)
##   Primary splits:
##       age               < 67.5   to the right, improve=2.3273090, (0 missing)
##       reimbursement2008 < 11575  to the left,  improve=1.8244140, (0 missing)
##       stroke            < 0.5    to the right, improve=0.5034792, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.4237564, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.3937905, (0 missing)
## 
## Node number 7943: 19 observations
##   predicted class=B3  expected loss=0.5263158  P(node) =6.914044e-05
##     class counts:     2     4     9     4     0
##    probabilities: 0.105 0.211 0.474 0.211 0.000 
## 
## Node number 7960: 349 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.6475645  P(node) =0.001270001
##     class counts:   123   103    55    57    11
##    probabilities: 0.352 0.295 0.158 0.163 0.032 
##   left son=15920 (331 obs) right son=15921 (18 obs)
##   Primary splits:
##       age               < 54     to the right, improve=2.0196730, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.9958000, (0 missing)
##       reimbursement2008 < 15235  to the left,  improve=1.7314800, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.9381122, (0 missing)
##       copd              < 0.5    to the right, improve=0.4818854, (0 missing)
## 
## Node number 7961: 885 observations
##   predicted class=B2  expected loss=0.6440678  P(node) =0.003220489
##     class counts:   251   315   197   103    19
##    probabilities: 0.284 0.356 0.223 0.116 0.021 
## 
## Node number 7962: 547 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.6709324  P(node) =0.001990517
##     class counts:   154   180   136    65    12
##    probabilities: 0.282 0.329 0.249 0.119 0.022 
##   left son=15924 (310 obs) right son=15925 (237 obs)
##   Primary splits:
##       reimbursement2008 < 9205   to the right, improve=2.7787810, (0 missing)
##       age               < 68.5   to the right, improve=2.5123800, (0 missing)
##       bucket2008        < 2.5    to the right, improve=0.9624740, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.6055315, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.5192530, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.835, adj=0.620, (0 split)
##       age        < 44.5   to the right, agree=0.581, adj=0.034, (0 split)
## 
## Node number 7963: 643 observations
##   predicted class=B2  expected loss=0.6391913  P(node) =0.002339858
##     class counts:   135   232   159   110     7
##    probabilities: 0.210 0.361 0.247 0.171 0.011 
## 
## Node number 8140: 457 observations,    complexity param=7.379774e-05
##   predicted class=B1  expected loss=0.7133479  P(node) =0.00166301
##     class counts:   131   107    73   120    26
##    probabilities: 0.287 0.234 0.160 0.263 0.057 
##   left son=16280 (398 obs) right son=16281 (59 obs)
##   Primary splits:
##       age               < 86.5   to the left,  improve=2.218362, (0 missing)
##       ihd               < 0.5    to the right, improve=2.044330, (0 missing)
##       reimbursement2008 < 19430  to the right, improve=1.853412, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.735004, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=1.352826, (0 missing)
## 
## Node number 8141: 11 observations
##   predicted class=B2  expected loss=0.5454545  P(node) =4.002868e-05
##     class counts:     0     5     4     2     0
##    probabilities: 0.000 0.455 0.364 0.182 0.000 
## 
## Node number 13216: 235 observations
##   predicted class=B1  expected loss=0.4510638  P(node) =0.0008551581
##     class counts:   129    64    30    11     1
##    probabilities: 0.549 0.272 0.128 0.047 0.004 
## 
## Node number 13217: 16 observations
##   predicted class=B2  expected loss=0.4375  P(node) =5.822353e-05
##     class counts:     4     9     3     0     0
##    probabilities: 0.250 0.562 0.188 0.000 0.000 
## 
## Node number 13220: 201 observations
##   predicted class=B1  expected loss=0.5472637  P(node) =0.0007314331
##     class counts:    91    67    29    14     0
##    probabilities: 0.453 0.333 0.144 0.070 0.000 
## 
## Node number 13221: 12 observations
##   predicted class=B2  expected loss=0.4166667  P(node) =4.366765e-05
##     class counts:     2     7     1     1     1
##    probabilities: 0.167 0.583 0.083 0.083 0.083 
## 
## Node number 13882: 472 observations,    complexity param=5.165842e-05
##   predicted class=B1  expected loss=0.5275424  P(node) =0.001717594
##     class counts:   223   163    57    25     4
##    probabilities: 0.472 0.345 0.121 0.053 0.008 
##   left son=27764 (343 obs) right son=27765 (129 obs)
##   Primary splits:
##       age               < 73.5   to the right, improve=4.630612, (0 missing)
##       reimbursement2008 < 2805   to the right, improve=1.597068, (0 missing)
##       depression        < 0.5    to the left,  improve=1.459900, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.335760, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.130037, (0 missing)
## 
## Node number 13883: 297 observations,    complexity param=6.088314e-05
##   predicted class=B2  expected loss=0.5622896  P(node) =0.001080774
##     class counts:   119   130    35    13     0
##    probabilities: 0.401 0.438 0.118 0.044 0.000 
##   left son=27766 (218 obs) right son=27767 (79 obs)
##   Primary splits:
##       osteoporosis      < 0.5    to the left,  improve=1.3951400, (0 missing)
##       reimbursement2008 < 2945   to the right, improve=1.0350230, (0 missing)
##       depression        < 0.5    to the left,  improve=0.9259259, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.7583938, (0 missing)
##       copd              < 0.5    to the left,  improve=0.3569379, (0 missing)
## 
## Node number 14000: 808 observations
##   predicted class=B1  expected loss=0.5569307  P(node) =0.002940288
##     class counts:   358   273   111    61     5
##    probabilities: 0.443 0.338 0.137 0.075 0.006 
## 
## Node number 14001: 266 observations,    complexity param=7.19528e-05
##   predicted class=B2  expected loss=0.6240602  P(node) =0.0009679661
##     class counts:    96   100    55    13     2
##    probabilities: 0.361 0.376 0.207 0.049 0.008 
##   left son=28002 (192 obs) right son=28003 (74 obs)
##   Primary splits:
##       reimbursement2008 < 2540   to the right, improve=2.9691060, (0 missing)
##       age               < 78.5   to the left,  improve=2.6852920, (0 missing)
##       cancer            < 0.5    to the right, improve=2.3754980, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.7018574, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.6157537, (0 missing)
##   Surrogate splits:
##       age < 50.5   to the right, agree=0.737, adj=0.054, (0 split)
## 
## Node number 14032: 15 observations
##   predicted class=B1  expected loss=0.3333333  P(node) =5.458456e-05
##     class counts:    10     2     3     0     0
##    probabilities: 0.667 0.133 0.200 0.000 0.000 
## 
## Node number 14033: 214 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.6121495  P(node) =0.0007787397
##     class counts:    83    80    41     8     2
##    probabilities: 0.388 0.374 0.192 0.037 0.009 
##   left son=28066 (169 obs) right son=28067 (45 obs)
##   Primary splits:
##       reimbursement2008 < 2515   to the left,  improve=1.6030020, (0 missing)
##       age               < 52.5   to the right, improve=0.9765448, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.7668533, (0 missing)
##       copd              < 0.5    to the right, improve=0.3681910, (0 missing)
##       ihd               < 0.5    to the right, improve=0.1207875, (0 missing)
## 
## Node number 14034: 14 observations
##   predicted class=B1  expected loss=0.3571429  P(node) =5.094559e-05
##     class counts:     9     2     2     1     0
##    probabilities: 0.643 0.143 0.143 0.071 0.000 
## 
## Node number 14035: 139 observations
##   predicted class=B2  expected loss=0.5683453  P(node) =0.0005058169
##     class counts:    40    60    28     9     2
##    probabilities: 0.288 0.432 0.201 0.065 0.014 
## 
## Node number 14176: 44 observations
##   predicted class=B2  expected loss=0.3863636  P(node) =0.0001601147
##     class counts:    14    27     2     1     0
##    probabilities: 0.318 0.614 0.045 0.023 0.000 
## 
## Node number 14177: 231 observations,    complexity param=7.748763e-05
##   predicted class=B1  expected loss=0.5930736  P(node) =0.0008406022
##     class counts:    94    94    32    10     1
##    probabilities: 0.407 0.407 0.139 0.043 0.004 
##   left son=28354 (169 obs) right son=28355 (62 obs)
##   Primary splits:
##       alzheimers        < 0.5    to the left,  improve=2.4583990, (0 missing)
##       reimbursement2008 < 2555   to the right, improve=1.0376560, (0 missing)
##       age               < 84.5   to the left,  improve=1.0243680, (0 missing)
##       copd              < 0.5    to the left,  improve=0.7240171, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.4410819, (0 missing)
##   Surrogate splits:
##       age < 87.5   to the left,  agree=0.745, adj=0.048, (0 split)
## 
## Node number 14178: 10 observations
##   predicted class=B1  expected loss=0.3  P(node) =3.63897e-05
##     class counts:     7     2     1     0     0
##    probabilities: 0.700 0.200 0.100 0.000 0.000 
## 
## Node number 14179: 723 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5809129  P(node) =0.002630976
##     class counts:   303   277    98    42     3
##    probabilities: 0.419 0.383 0.136 0.058 0.004 
##   left son=28358 (689 obs) right son=28359 (34 obs)
##   Primary splits:
##       age               < 90.5   to the left,  improve=1.6650270, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.5078050, (0 missing)
##       reimbursement2008 < 2495   to the right, improve=0.8133392, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.6699213, (0 missing)
##       depression        < 0.5    to the left,  improve=0.5296598, (0 missing)
## 
## Node number 15404: 309 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5728155  P(node) =0.001124442
##     class counts:   132   117    38    20     2
##    probabilities: 0.427 0.379 0.123 0.065 0.006 
##   left son=30808 (253 obs) right son=30809 (56 obs)
##   Primary splits:
##       reimbursement2008 < 4635   to the right, improve=2.0908250, (0 missing)
##       age               < 73.5   to the right, improve=1.8355900, (0 missing)
##       depression        < 0.5    to the left,  improve=0.6554201, (0 missing)
##       copd              < 0.5    to the left,  improve=0.3380891, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.2757170, (0 missing)
## 
## Node number 15405: 21 observations
##   predicted class=B1  expected loss=0.5238095  P(node) =7.641838e-05
##     class counts:    10     2     6     3     0
##    probabilities: 0.476 0.095 0.286 0.143 0.000 
## 
## Node number 15446: 1527 observations,    complexity param=9.962695e-05
##   predicted class=B2  expected loss=0.5854617  P(node) =0.005556708
##     class counts:   613   633   203    72     6
##    probabilities: 0.401 0.415 0.133 0.047 0.004 
##   left son=30892 (1478 obs) right son=30893 (49 obs)
##   Primary splits:
##       reimbursement2008 < 3465   to the right, improve=1.7561930, (0 missing)
##       age               < 59.5   to the right, improve=1.2446620, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.3864080, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.3262151, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.1237742, (0 missing)
## 
## Node number 15447: 40 observations
##   predicted class=B1  expected loss=0.65  P(node) =0.0001455588
##     class counts:    14    12    12     2     0
##    probabilities: 0.350 0.300 0.300 0.050 0.000 
## 
## Node number 15450: 703 observations,    complexity param=8.855729e-05
##   predicted class=B1  expected loss=0.598862  P(node) =0.002558196
##     class counts:   282   244   119    53     5
##    probabilities: 0.401 0.347 0.169 0.075 0.007 
##   left son=30900 (298 obs) right son=30901 (405 obs)
##   Primary splits:
##       reimbursement2008 < 6635   to the right, improve=1.9072210, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.4867600, (0 missing)
##       age               < 74.5   to the left,  improve=1.1374550, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.4608058, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.4586126, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.596, adj=0.047, (0 split)
##       age        < 35.5   to the left,  agree=0.578, adj=0.003, (0 split)
## 
## Node number 15451: 34 observations
##   predicted class=B2  expected loss=0.5  P(node) =0.000123725
##     class counts:     8    17     4     5     0
##    probabilities: 0.235 0.500 0.118 0.147 0.000 
## 
## Node number 15452: 297 observations,    complexity param=7.19528e-05
##   predicted class=B1  expected loss=0.5757576  P(node) =0.001080774
##     class counts:   126   113    45    12     1
##    probabilities: 0.424 0.380 0.152 0.040 0.003 
##   left son=30904 (274 obs) right son=30905 (23 obs)
##   Primary splits:
##       reimbursement2008 < 5065   to the left,  improve=2.3768610, (0 missing)
##       alzheimers        < 0.5    to the right, improve=2.2936150, (0 missing)
##       age               < 37.5   to the left,  improve=1.9456180, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=0.7719678, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.6098027, (0 missing)
## 
## Node number 15453: 700 observations
##   predicted class=B2  expected loss=0.5814286  P(node) =0.002547279
##     class counts:   231   293   126    45     5
##    probabilities: 0.330 0.419 0.180 0.064 0.007 
## 
## Node number 15458: 889 observations
##   predicted class=B2  expected loss=0.5714286  P(node) =0.003235045
##     class counts:   327   381   133    45     3
##    probabilities: 0.368 0.429 0.150 0.051 0.003 
## 
## Node number 15459: 1053 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5318139  P(node) =0.003831836
##     class counts:   315   493   175    64     6
##    probabilities: 0.299 0.468 0.166 0.061 0.006 
##   left son=30918 (721 obs) right son=30919 (332 obs)
##   Primary splits:
##       osteoporosis      < 0.5    to the left,  improve=2.319421, (0 missing)
##       age               < 65.5   to the right, improve=2.157808, (0 missing)
##       reimbursement2008 < 4195   to the left,  improve=2.005955, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.694776, (0 missing)
##       bucket2008        < 2.5    to the right, improve=0.685891, (0 missing)
## 
## Node number 15480: 83 observations
##   predicted class=B2  expected loss=0.5662651  P(node) =0.0003020345
##     class counts:    29    36     8    10     0
##    probabilities: 0.349 0.434 0.096 0.120 0.000 
## 
## Node number 15481: 556 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.6330935  P(node) =0.002023268
##     class counts:   204   185   116    49     2
##    probabilities: 0.367 0.333 0.209 0.088 0.004 
##   left son=30962 (368 obs) right son=30963 (188 obs)
##   Primary splits:
##       age               < 67.5   to the right, improve=1.7538220, (0 missing)
##       reimbursement2008 < 17290  to the right, improve=1.5233210, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.8892958, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.8663588, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=0.8033839, (0 missing)
## 
## Node number 15484: 40 observations
##   predicted class=B1  expected loss=0.425  P(node) =0.0001455588
##     class counts:    23     8     7     2     0
##    probabilities: 0.575 0.200 0.175 0.050 0.000 
## 
## Node number 15485: 82 observations
##   predicted class=B2  expected loss=0.6219512  P(node) =0.0002983956
##     class counts:    26    31    15    10     0
##    probabilities: 0.317 0.378 0.183 0.122 0.000 
## 
## Node number 15884: 93 observations,    complexity param=5.811572e-05
##   predicted class=B2  expected loss=0.5913978  P(node) =0.0003384243
##     class counts:    32    38    15     8     0
##    probabilities: 0.344 0.409 0.161 0.086 0.000 
##   left son=31768 (44 obs) right son=31769 (49 obs)
##   Primary splits:
##       reimbursement2008 < 6110   to the right, improve=2.8038180, (0 missing)
##       age               < 68.5   to the right, improve=0.9063337, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.4118188, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.3578690, (0 missing)
##       stroke            < 0.5    to the right, improve=0.3151562, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the right, agree=0.785, adj=0.545, (0 split)
##       age        < 73.5   to the left,  agree=0.602, adj=0.159, (0 split)
##       alzheimers < 0.5    to the right, agree=0.538, adj=0.023, (0 split)
## 
## Node number 15885: 13 observations
##   predicted class=B1  expected loss=0.4615385  P(node) =4.730662e-05
##     class counts:     7     1     1     4     0
##    probabilities: 0.538 0.077 0.077 0.308 0.000 
## 
## Node number 15920: 331 observations
##   predicted class=B1  expected loss=0.6344411  P(node) =0.001204499
##     class counts:   121    94    53    53    10
##    probabilities: 0.366 0.284 0.160 0.160 0.030 
## 
## Node number 15921: 18 observations
##   predicted class=B2  expected loss=0.5  P(node) =6.550147e-05
##     class counts:     2     9     2     4     1
##    probabilities: 0.111 0.500 0.111 0.222 0.056 
## 
## Node number 15924: 310 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.6741935  P(node) =0.001128081
##     class counts:   101    99    64    37     9
##    probabilities: 0.326 0.319 0.206 0.119 0.029 
##   left son=31848 (50 obs) right son=31849 (260 obs)
##   Primary splits:
##       reimbursement2008 < 9955   to the left,  improve=3.5194040, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.4052180, (0 missing)
##       age               < 60.5   to the right, improve=1.3545900, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.9417634, (0 missing)
##       stroke            < 0.5    to the right, improve=0.4401818, (0 missing)
## 
## Node number 15925: 237 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.6582278  P(node) =0.000862436
##     class counts:    53    81    72    28     3
##    probabilities: 0.224 0.342 0.304 0.118 0.013 
##   left son=31850 (56 obs) right son=31851 (181 obs)
##   Primary splits:
##       age               < 67.5   to the left,  improve=3.14488400, (0 missing)
##       reimbursement2008 < 7130   to the left,  improve=2.11196700, (0 missing)
##       bucket2008        < 2.5    to the right, improve=0.86604090, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.39390990, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.05008339, (0 missing)
## 
## Node number 16280: 398 observations,    complexity param=7.379774e-05
##   predicted class=B1  expected loss=0.7236181  P(node) =0.00144831
##     class counts:   110   101    63    99    25
##    probabilities: 0.276 0.254 0.158 0.249 0.063 
##   left son=32560 (179 obs) right son=32561 (219 obs)
##   Primary splits:
##       alzheimers        < 0.5    to the right, improve=2.797541, (0 missing)
##       ihd               < 0.5    to the right, improve=2.182276, (0 missing)
##       reimbursement2008 < 15500  to the right, improve=1.710577, (0 missing)
##       stroke            < 0.5    to the right, improve=1.223226, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=1.211249, (0 missing)
##   Surrogate splits:
##       stroke            < 0.5    to the right, agree=0.563, adj=0.028, (0 split)
##       reimbursement2008 < 15625  to the left,  agree=0.563, adj=0.028, (0 split)
##       age               < 62.5   to the left,  agree=0.555, adj=0.011, (0 split)
##       osteoporosis      < 0.5    to the right, agree=0.553, adj=0.006, (0 split)
## 
## Node number 16281: 59 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.6440678  P(node) =0.0002146993
##     class counts:    21     6    10    21     1
##    probabilities: 0.356 0.102 0.169 0.356 0.017 
##   left son=32562 (18 obs) right son=32563 (41 obs)
##   Primary splits:
##       reimbursement2008 < 19680  to the right, improve=1.9754260, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.9501021, (0 missing)
##       bucket2008        < 3.5    to the right, improve=0.8931654, (0 missing)
##       age               < 90.5   to the right, improve=0.7250257, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.5260164, (0 missing)
##   Surrogate splits:
##       bucket2008 < 3.5    to the right, agree=0.847, adj=0.5, (0 split)
## 
## Node number 27764: 343 observations,    complexity param=5.165842e-05
##   predicted class=B1  expected loss=0.5568513  P(node) =0.001248167
##     class counts:   152   136    37    16     2
##    probabilities: 0.443 0.397 0.108 0.047 0.006 
##   left son=55528 (117 obs) right son=55529 (226 obs)
##   Primary splits:
##       reimbursement2008 < 2835   to the right, improve=1.9282960, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.1581140, (0 missing)
##       age               < 82.5   to the right, improve=1.0933820, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.0145490, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.9380155, (0 missing)
## 
## Node number 27765: 129 observations
##   predicted class=B1  expected loss=0.4496124  P(node) =0.0004694272
##     class counts:    71    27    20     9     2
##    probabilities: 0.550 0.209 0.155 0.070 0.016 
## 
## Node number 27766: 218 observations,    complexity param=6.088314e-05
##   predicted class=B1  expected loss=0.5733945  P(node) =0.0007932956
##     class counts:    93    89    28     8     0
##    probabilities: 0.427 0.408 0.128 0.037 0.000 
##   left son=55532 (194 obs) right son=55533 (24 obs)
##   Primary splits:
##       reimbursement2008 < 2945   to the left,  improve=1.9617420, (0 missing)
##       depression        < 0.5    to the left,  improve=0.6526821, (0 missing)
##       kidney            < 0.5    to the left,  improve=0.4610298, (0 missing)
##       age               < 57.5   to the left,  improve=0.4574831, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.3559027, (0 missing)
## 
## Node number 27767: 79 observations
##   predicted class=B2  expected loss=0.4810127  P(node) =0.0002874787
##     class counts:    26    41     7     5     0
##    probabilities: 0.329 0.519 0.089 0.063 0.000 
## 
## Node number 28002: 192 observations,    complexity param=7.19528e-05
##   predicted class=B2  expected loss=0.59375  P(node) =0.0006986823
##     class counts:    72    78    29    11     2
##    probabilities: 0.375 0.406 0.151 0.057 0.010 
##   left son=56004 (124 obs) right son=56005 (68 obs)
##   Primary splits:
##       age               < 78.5   to the left,  improve=3.1968100, (0 missing)
##       reimbursement2008 < 2885   to the left,  improve=2.1236740, (0 missing)
##       alzheimers        < 0.5    to the right, improve=1.0053880, (0 missing)
##       bucket2008        < 1.5    to the left,  improve=0.7479369, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.5316513, (0 missing)
## 
## Node number 28003: 74 observations,    complexity param=7.19528e-05
##   predicted class=B3  expected loss=0.6486486  P(node) =0.0002692838
##     class counts:    24    22    26     2     0
##    probabilities: 0.324 0.297 0.351 0.027 0.000 
##   left son=56006 (8 obs) right son=56007 (66 obs)
##   Primary splits:
##       cancer            < 0.5    to the right, improve=6.4864860, (0 missing)
##       age               < 65     to the left,  improve=1.7666590, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.7622440, (0 missing)
##       reimbursement2008 < 2355   to the right, improve=1.0927360, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.8745462, (0 missing)
## 
## Node number 28066: 169 observations
##   predicted class=B1  expected loss=0.5798817  P(node) =0.000614986
##     class counts:    71    58    32     6     2
##    probabilities: 0.420 0.343 0.189 0.036 0.012 
## 
## Node number 28067: 45 observations
##   predicted class=B2  expected loss=0.5111111  P(node) =0.0001637537
##     class counts:    12    22     9     2     0
##    probabilities: 0.267 0.489 0.200 0.044 0.000 
## 
## Node number 28354: 169 observations
##   predicted class=B1  expected loss=0.5621302  P(node) =0.000614986
##     class counts:    74    60    26     8     1
##    probabilities: 0.438 0.355 0.154 0.047 0.006 
## 
## Node number 28355: 62 observations
##   predicted class=B2  expected loss=0.4516129  P(node) =0.0002256162
##     class counts:    20    34     6     2     0
##    probabilities: 0.323 0.548 0.097 0.032 0.000 
## 
## Node number 28358: 689 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5718433  P(node) =0.002507251
##     class counts:   295   261    92    38     3
##    probabilities: 0.428 0.379 0.134 0.055 0.004 
##   left son=56716 (367 obs) right son=56717 (322 obs)
##   Primary splits:
##       heart.failure     < 0.5    to the left,  improve=1.5366830, (0 missing)
##       reimbursement2008 < 2185   to the right, improve=0.9498001, (0 missing)
##       age               < 67.5   to the left,  improve=0.9450906, (0 missing)
##       copd              < 0.5    to the left,  improve=0.5052370, (0 missing)
##       depression        < 0.5    to the left,  improve=0.4336301, (0 missing)
##   Surrogate splits:
##       copd              < 0.5    to the left,  agree=0.605, adj=0.155, (0 split)
##       age               < 85.5   to the left,  agree=0.543, adj=0.022, (0 split)
##       reimbursement2008 < 2515   to the left,  agree=0.541, adj=0.019, (0 split)
##       alzheimers        < 0.5    to the left,  agree=0.538, adj=0.012, (0 split)
## 
## Node number 28359: 34 observations
##   predicted class=B2  expected loss=0.5294118  P(node) =0.000123725
##     class counts:     8    16     6     4     0
##    probabilities: 0.235 0.471 0.176 0.118 0.000 
## 
## Node number 30808: 253 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5454545  P(node) =0.0009206595
##     class counts:   115    89    30    17     2
##    probabilities: 0.455 0.352 0.119 0.067 0.008 
##   left son=61616 (245 obs) right son=61617 (8 obs)
##   Primary splits:
##       age               < 96     to the left,  improve=1.6668230, (0 missing)
##       reimbursement2008 < 8170   to the left,  improve=1.5801570, (0 missing)
##       depression        < 0.5    to the left,  improve=0.8012407, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.6406559, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.4810539, (0 missing)
## 
## Node number 30809: 56 observations
##   predicted class=B2  expected loss=0.5  P(node) =0.0002037823
##     class counts:    17    28     8     3     0
##    probabilities: 0.304 0.500 0.143 0.054 0.000 
## 
## Node number 30892: 1478 observations,    complexity param=9.962695e-05
##   predicted class=B2  expected loss=0.5886333  P(node) =0.005378398
##     class counts:   600   608   199    67     4
##    probabilities: 0.406 0.411 0.135 0.045 0.003 
##   left son=61784 (759 obs) right son=61785 (719 obs)
##   Primary splits:
##       reimbursement2008 < 4655   to the left,  improve=1.4912330, (0 missing)
##       age               < 59.5   to the right, improve=1.4379920, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.4252592, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.4189515, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.1287486, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.566, adj=0.108, (0 split)
##       alzheimers < 0.5    to the left,  agree=0.535, adj=0.043, (0 split)
##       stroke     < 0.5    to the left,  agree=0.533, adj=0.040, (0 split)
##       copd       < 0.5    to the left,  agree=0.530, adj=0.033, (0 split)
##       age        < 82.5   to the left,  agree=0.526, adj=0.025, (0 split)
## 
## Node number 30893: 49 observations
##   predicted class=B2  expected loss=0.4897959  P(node) =0.0001783096
##     class counts:    13    25     4     5     2
##    probabilities: 0.265 0.510 0.082 0.102 0.041 
## 
## Node number 30900: 298 observations
##   predicted class=B1  expected loss=0.5503356  P(node) =0.001084413
##     class counts:   134    94    46    20     4
##    probabilities: 0.450 0.315 0.154 0.067 0.013 
## 
## Node number 30901: 405 observations,    complexity param=8.855729e-05
##   predicted class=B2  expected loss=0.6296296  P(node) =0.001473783
##     class counts:   148   150    73    33     1
##    probabilities: 0.365 0.370 0.180 0.081 0.002 
##   left son=61802 (137 obs) right son=61803 (268 obs)
##   Primary splits:
##       reimbursement2008 < 5685   to the left,  improve=1.4352860, (0 missing)
##       age               < 43     to the right, improve=1.2563810, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=0.8108852, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.3644866, (0 missing)
##       copd              < 0.5    to the left,  improve=0.3421456, (0 missing)
## 
## Node number 30904: 274 observations,    complexity param=7.19528e-05
##   predicted class=B1  expected loss=0.5583942  P(node) =0.0009970779
##     class counts:   121    99    42    11     1
##    probabilities: 0.442 0.361 0.153 0.040 0.004 
##   left son=61808 (174 obs) right son=61809 (100 obs)
##   Primary splits:
##       alzheimers        < 0.5    to the left,  improve=2.2349370, (0 missing)
##       age               < 37.5   to the left,  improve=1.7714310, (0 missing)
##       reimbursement2008 < 4990   to the right, improve=1.7636660, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=0.8701440, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.4025273, (0 missing)
##   Surrogate splits:
##       stroke            < 0.5    to the left,  agree=0.668, adj=0.09, (0 split)
##       reimbursement2008 < 3085   to the right, agree=0.642, adj=0.02, (0 split)
## 
## Node number 30905: 23 observations
##   predicted class=B2  expected loss=0.3913043  P(node) =8.369632e-05
##     class counts:     5    14     3     1     0
##    probabilities: 0.217 0.609 0.130 0.043 0.000 
## 
## Node number 30918: 721 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5492372  P(node) =0.002623698
##     class counts:   234   325   114    44     4
##    probabilities: 0.325 0.451 0.158 0.061 0.006 
##   left son=61836 (109 obs) right son=61837 (612 obs)
##   Primary splits:
##       age               < 86.5   to the right, improve=5.2024390, (0 missing)
##       reimbursement2008 < 8105   to the left,  improve=1.9497410, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.3441110, (0 missing)
##       bucket2008        < 2.5    to the right, improve=0.8592657, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.1415473, (0 missing)
## 
## Node number 30919: 332 observations
##   predicted class=B2  expected loss=0.4939759  P(node) =0.001208138
##     class counts:    81   168    61    20     2
##    probabilities: 0.244 0.506 0.184 0.060 0.006 
## 
## Node number 30962: 368 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.625  P(node) =0.001339141
##     class counts:   138   132    66    32     0
##    probabilities: 0.375 0.359 0.179 0.087 0.000 
##   left son=61924 (261 obs) right son=61925 (107 obs)
##   Primary splits:
##       reimbursement2008 < 10440  to the right, improve=2.0386870, (0 missing)
##       age               < 68.5   to the right, improve=2.0238320, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=1.0604210, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.8507150, (0 missing)
##       heart.failure     < 0.5    to the right, improve=0.3195541, (0 missing)
## 
## Node number 30963: 188 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.6489362  P(node) =0.0006841264
##     class counts:    66    53    50    17     2
##    probabilities: 0.351 0.282 0.266 0.090 0.011 
##   left son=61926 (135 obs) right son=61927 (53 obs)
##   Primary splits:
##       age               < 55.5   to the right, improve=1.3142350, (0 missing)
##       reimbursement2008 < 8995   to the left,  improve=1.1323620, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.7672950, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.7658279, (0 missing)
##       bucket2008        < 3.5    to the right, improve=0.3998270, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 8645   to the right, agree=0.723, adj=0.019, (0 split)
## 
## Node number 31768: 44 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5227273  P(node) =0.0001601147
##     class counts:    21    13     5     5     0
##    probabilities: 0.477 0.295 0.114 0.114 0.000 
##   left son=63536 (26 obs) right son=63537 (18 obs)
##   Primary splits:
##       reimbursement2008 < 9180   to the left,  improve=3.34188000, (0 missing)
##       age               < 73.5   to the right, improve=1.53473700, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=1.08333300, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.99564270, (0 missing)
##       copd              < 0.5    to the right, improve=0.09090909, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.864, adj=0.667, (0 split)
##       age        < 82.5   to the left,  agree=0.636, adj=0.111, (0 split)
##       stroke     < 0.5    to the left,  agree=0.614, adj=0.056, (0 split)
## 
## Node number 31769: 49 observations
##   predicted class=B2  expected loss=0.4897959  P(node) =0.0001783096
##     class counts:    11    25    10     3     0
##    probabilities: 0.224 0.510 0.204 0.061 0.000 
## 
## Node number 31848: 50 observations
##   predicted class=B2  expected loss=0.56  P(node) =0.0001819485
##     class counts:    21    22     1     6     0
##    probabilities: 0.420 0.440 0.020 0.120 0.000 
## 
## Node number 31849: 260 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.6923077  P(node) =0.0009461323
##     class counts:    80    77    63    31     9
##    probabilities: 0.308 0.296 0.242 0.119 0.035 
##   left son=63698 (20 obs) right son=63699 (240 obs)
##   Primary splits:
##       reimbursement2008 < 14765  to the right, improve=1.866026, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.724179, (0 missing)
##       age               < 59     to the right, improve=1.389622, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.186623, (0 missing)
##       stroke            < 0.5    to the right, improve=0.396978, (0 missing)
## 
## Node number 31850: 56 observations
##   predicted class=B2  expected loss=0.5178571  P(node) =0.0002037823
##     class counts:    11    27     9     9     0
##    probabilities: 0.196 0.482 0.161 0.161 0.000 
## 
## Node number 31851: 181 observations,    complexity param=5.534831e-05
##   predicted class=B3  expected loss=0.6519337  P(node) =0.0006586537
##     class counts:    42    54    63    19     3
##    probabilities: 0.232 0.298 0.348 0.105 0.017 
##   left son=63702 (136 obs) right son=63703 (45 obs)
##   Primary splits:
##       reimbursement2008 < 6865   to the right, improve=2.6510090, (0 missing)
##       age               < 95.5   to the left,  improve=1.1712710, (0 missing)
##       bucket2008        < 2.5    to the right, improve=0.4758931, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.1841866, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.1010412, (0 missing)
## 
## Node number 32560: 179 observations,    complexity param=7.379774e-05
##   predicted class=B1  expected loss=0.6815642  P(node) =0.0006513757
##     class counts:    57    51    27    31    13
##    probabilities: 0.318 0.285 0.151 0.173 0.073 
##   left son=65120 (38 obs) right son=65121 (141 obs)
##   Primary splits:
##       reimbursement2008 < 21440  to the right, improve=2.8400160, (0 missing)
##       age               < 70.5   to the left,  improve=1.0471050, (0 missing)
##       stroke            < 0.5    to the right, improve=0.8887163, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.8119666, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.6975642, (0 missing)
## 
## Node number 32561: 219 observations
##   predicted class=B4  expected loss=0.6894977  P(node) =0.0007969345
##     class counts:    53    50    36    68    12
##    probabilities: 0.242 0.228 0.164 0.311 0.055 
## 
## Node number 32562: 18 observations
##   predicted class=B1  expected loss=0.4444444  P(node) =6.550147e-05
##     class counts:    10     2     0     5     1
##    probabilities: 0.556 0.111 0.000 0.278 0.056 
## 
## Node number 32563: 41 observations
##   predicted class=B4  expected loss=0.6097561  P(node) =0.0001491978
##     class counts:    11     4    10    16     0
##    probabilities: 0.268 0.098 0.244 0.390 0.000 
## 
## Node number 55528: 117 observations,    complexity param=5.165842e-05
##   predicted class=B1  expected loss=0.4871795  P(node) =0.0004257595
##     class counts:    60    38    15     3     1
##    probabilities: 0.513 0.325 0.128 0.026 0.009 
##   left son=111056 (78 obs) right son=111057 (39 obs)
##   Primary splits:
##       reimbursement2008 < 2945   to the left,  improve=2.9829060, (0 missing)
##       kidney            < 0.5    to the left,  improve=1.2210830, (0 missing)
##       age               < 76.5   to the right, improve=1.1210830, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.9103070, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.2543679, (0 missing)
##   Surrogate splits:
##       age < 76.5   to the right, agree=0.684, adj=0.051, (0 split)
## 
## Node number 55529: 226 observations,    complexity param=5.165842e-05
##   predicted class=B2  expected loss=0.5663717  P(node) =0.0008224073
##     class counts:    92    98    22    13     1
##    probabilities: 0.407 0.434 0.097 0.058 0.004 
##   left son=111058 (72 obs) right son=111059 (154 obs)
##   Primary splits:
##       age               < 80.5   to the right, improve=1.5914710, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.3555880, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.7668454, (0 missing)
##       reimbursement2008 < 2795   to the left,  improve=0.6874895, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.4980787, (0 missing)
## 
## Node number 55532: 194 observations
##   predicted class=B1  expected loss=0.556701  P(node) =0.0007059603
##     class counts:    86    74    27     7     0
##    probabilities: 0.443 0.381 0.139 0.036 0.000 
## 
## Node number 55533: 24 observations
##   predicted class=B2  expected loss=0.375  P(node) =8.733529e-05
##     class counts:     7    15     1     1     0
##    probabilities: 0.292 0.625 0.042 0.042 0.000 
## 
## Node number 56004: 124 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5403226  P(node) =0.0004512323
##     class counts:    57    47    16     4     0
##    probabilities: 0.460 0.379 0.129 0.032 0.000 
##   left son=112008 (46 obs) right son=112009 (78 obs)
##   Primary splits:
##       age               < 72.5   to the right, improve=2.8817380, (0 missing)
##       reimbursement2008 < 2885   to the left,  improve=1.5254660, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.4454760, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.7103829, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.4023915, (0 missing)
##   Surrogate splits:
##       stroke            < 0.5    to the right, agree=0.661, adj=0.087, (0 split)
##       reimbursement2008 < 2575   to the left,  agree=0.645, adj=0.043, (0 split)
## 
## Node number 56005: 68 observations
##   predicted class=B2  expected loss=0.5441176  P(node) =0.00024745
##     class counts:    15    31    13     7     2
##    probabilities: 0.221 0.456 0.191 0.103 0.029 
## 
## Node number 56006: 8 observations
##   predicted class=B2  expected loss=0  P(node) =2.911176e-05
##     class counts:     0     8     0     0     0
##    probabilities: 0.000 1.000 0.000 0.000 0.000 
## 
## Node number 56007: 66 observations,    complexity param=5.534831e-05
##   predicted class=B3  expected loss=0.6060606  P(node) =0.0002401721
##     class counts:    24    14    26     2     0
##    probabilities: 0.364 0.212 0.394 0.030 0.000 
##   left son=112014 (40 obs) right son=112015 (26 obs)
##   Primary splits:
##       alzheimers        < 0.5    to the left,  improve=2.3576920, (0 missing)
##       age               < 65     to the left,  improve=1.8352940, (0 missing)
##       reimbursement2008 < 2375   to the right, improve=1.1494920, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=0.1363636, (0 missing)
##   Surrogate splits:
##       age < 82.5   to the left,  agree=0.652, adj=0.115, (0 split)
## 
## Node number 56716: 367 observations
##   predicted class=B1  expected loss=0.5395095  P(node) =0.001335502
##     class counts:   169   131    51    13     3
##    probabilities: 0.460 0.357 0.139 0.035 0.008 
## 
## Node number 56717: 322 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.5962733  P(node) =0.001171748
##     class counts:   126   130    41    25     0
##    probabilities: 0.391 0.404 0.127 0.078 0.000 
##   left son=113434 (78 obs) right son=113435 (244 obs)
##   Primary splits:
##       age               < 67.5   to the left,  improve=2.0124890, (0 missing)
##       reimbursement2008 < 2265   to the right, improve=1.1949400, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.3273471, (0 missing)
##       depression        < 0.5    to the right, improve=0.1786959, (0 missing)
##       copd              < 0.5    to the left,  improve=0.1745923, (0 missing)
## 
## Node number 61616: 245 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.5346939  P(node) =0.0008915478
##     class counts:   114    87    27    16     1
##    probabilities: 0.465 0.355 0.110 0.065 0.004 
##   left son=123232 (209 obs) right son=123233 (36 obs)
##   Primary splits:
##       reimbursement2008 < 8170   to the left,  improve=1.7182870, (0 missing)
##       age               < 90.5   to the right, improve=1.6062760, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.7459219, (0 missing)
##       depression        < 0.5    to the left,  improve=0.6596720, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.6366849, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.971, adj=0.806, (0 split)
## 
## Node number 61617: 8 observations
##   predicted class=B3  expected loss=0.625  P(node) =2.911176e-05
##     class counts:     1     2     3     1     1
##    probabilities: 0.125 0.250 0.375 0.125 0.125 
## 
## Node number 61784: 759 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5678524  P(node) =0.002761979
##     class counts:   328   303    94    33     1
##    probabilities: 0.432 0.399 0.124 0.043 0.001 
##   left son=123568 (158 obs) right son=123569 (601 obs)
##   Primary splits:
##       reimbursement2008 < 4315   to the right, improve=1.62186500, (0 missing)
##       age               < 82.5   to the right, improve=0.60286370, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.24697950, (0 missing)
##       copd              < 0.5    to the left,  improve=0.10233690, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.09394217, (0 missing)
## 
## Node number 61785: 719 observations,    complexity param=9.962695e-05
##   predicted class=B2  expected loss=0.5757997  P(node) =0.00261642
##     class counts:   272   305   105    34     3
##    probabilities: 0.378 0.424 0.146 0.047 0.004 
##   left son=123570 (346 obs) right son=123571 (373 obs)
##   Primary splits:
##       reimbursement2008 < 5835   to the left,  improve=2.8015510, (0 missing)
##       age               < 59.5   to the right, improve=2.2849680, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.5855315, (0 missing)
##       stroke            < 0.5    to the right, improve=0.5109046, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.2469968, (0 missing)
##   Surrogate splits:
##       bucket2008 < 2.5    to the left,  agree=0.590, adj=0.147, (0 split)
##       alzheimers < 0.5    to the left,  agree=0.537, adj=0.038, (0 split)
##       age        < 60.5   to the left,  agree=0.527, adj=0.017, (0 split)
## 
## Node number 61802: 137 observations
##   predicted class=B1  expected loss=0.5839416  P(node) =0.000498539
##     class counts:    57    43    22    15     0
##    probabilities: 0.416 0.314 0.161 0.109 0.000 
## 
## Node number 61803: 268 observations
##   predicted class=B2  expected loss=0.6007463  P(node) =0.0009752441
##     class counts:    91   107    51    18     1
##    probabilities: 0.340 0.399 0.190 0.067 0.004 
## 
## Node number 61808: 174 observations
##   predicted class=B1  expected loss=0.5229885  P(node) =0.0006331809
##     class counts:    83    53    29     8     1
##    probabilities: 0.477 0.305 0.167 0.046 0.006 
## 
## Node number 61809: 100 observations,    complexity param=7.19528e-05
##   predicted class=B2  expected loss=0.54  P(node) =0.000363897
##     class counts:    38    46    13     3     0
##    probabilities: 0.380 0.460 0.130 0.030 0.000 
##   left son=123618 (26 obs) right son=123619 (74 obs)
##   Primary splits:
##       reimbursement2008 < 4355   to the right, improve=5.3372560, (0 missing)
##       age               < 62.5   to the right, improve=1.9704690, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.2703500, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.5886275, (0 missing)
##   Surrogate splits:
##       age < 50.5   to the left,  agree=0.79, adj=0.192, (0 split)
## 
## Node number 61836: 109 observations
##   predicted class=B1  expected loss=0.5412844  P(node) =0.0003966478
##     class counts:    50    33    16     9     1
##    probabilities: 0.459 0.303 0.147 0.083 0.009 
## 
## Node number 61837: 612 observations
##   predicted class=B2  expected loss=0.5228758  P(node) =0.00222705
##     class counts:   184   292    98    35     3
##    probabilities: 0.301 0.477 0.160 0.057 0.005 
## 
## Node number 61924: 261 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5862069  P(node) =0.0009497713
##     class counts:   108    87    44    22     0
##    probabilities: 0.414 0.333 0.169 0.084 0.000 
##   left son=123848 (92 obs) right son=123849 (169 obs)
##   Primary splits:
##       reimbursement2008 < 12585  to the left,  improve=2.1315740, (0 missing)
##       age               < 77.5   to the right, improve=1.2761660, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.0543160, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=1.0296720, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.5202291, (0 missing)
## 
## Node number 61925: 107 observations
##   predicted class=B2  expected loss=0.5794393  P(node) =0.0003893698
##     class counts:    30    45    22    10     0
##    probabilities: 0.280 0.421 0.206 0.093 0.000 
## 
## Node number 61926: 135 observations
##   predicted class=B1  expected loss=0.6074074  P(node) =0.000491261
##     class counts:    53    34    36    12     0
##    probabilities: 0.393 0.252 0.267 0.089 0.000 
## 
## Node number 61927: 53 observations
##   predicted class=B2  expected loss=0.6415094  P(node) =0.0001928654
##     class counts:    13    19    14     5     2
##    probabilities: 0.245 0.358 0.264 0.094 0.038 
## 
## Node number 63536: 26 observations
##   predicted class=B1  expected loss=0.3461538  P(node) =9.461323e-05
##     class counts:    17     4     2     3     0
##    probabilities: 0.654 0.154 0.077 0.115 0.000 
## 
## Node number 63537: 18 observations
##   predicted class=B2  expected loss=0.5  P(node) =6.550147e-05
##     class counts:     4     9     3     2     0
##    probabilities: 0.222 0.500 0.167 0.111 0.000 
## 
## Node number 63698: 20 observations
##   predicted class=B1  expected loss=0.45  P(node) =7.277941e-05
##     class counts:    11     5     2     1     1
##    probabilities: 0.550 0.250 0.100 0.050 0.050 
## 
## Node number 63699: 240 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.7  P(node) =0.0008733529
##     class counts:    69    72    61    30     8
##    probabilities: 0.288 0.300 0.254 0.125 0.033 
##   left son=127398 (201 obs) right son=127399 (39 obs)
##   Primary splits:
##       age               < 61.5   to the right, improve=1.4580460, (0 missing)
##       reimbursement2008 < 10970  to the right, improve=1.4206140, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=1.0755290, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.9752886, (0 missing)
##       stroke            < 0.5    to the right, improve=0.4524283, (0 missing)
## 
## Node number 63702: 136 observations
##   predicted class=B3  expected loss=0.6029412  P(node) =0.0004949
##     class counts:    34    36    54    10     2
##    probabilities: 0.250 0.265 0.397 0.074 0.015 
## 
## Node number 63703: 45 observations
##   predicted class=B2  expected loss=0.6  P(node) =0.0001637537
##     class counts:     8    18     9     9     1
##    probabilities: 0.178 0.400 0.200 0.200 0.022 
## 
## Node number 65120: 38 observations
##   predicted class=B2  expected loss=0.5  P(node) =0.0001382809
##     class counts:     9    19     4     5     1
##    probabilities: 0.237 0.500 0.105 0.132 0.026 
## 
## Node number 65121: 141 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.6595745  P(node) =0.0005130948
##     class counts:    48    32    23    26    12
##    probabilities: 0.340 0.227 0.163 0.184 0.085 
##   left son=130242 (89 obs) right son=130243 (52 obs)
##   Primary splits:
##       reimbursement2008 < 17585  to the right, improve=2.1889060, (0 missing)
##       age               < 47.5   to the right, improve=1.2186760, (0 missing)
##       bucket2008        < 3.5    to the right, improve=1.1702130, (0 missing)
##       stroke            < 0.5    to the right, improve=0.9175166, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.5919705, (0 missing)
##   Surrogate splits:
##       bucket2008 < 3.5    to the right, agree=0.702, adj=0.192, (0 split)
##       age        < 47.5   to the right, agree=0.667, adj=0.096, (0 split)
## 
## Node number 111056: 78 observations
##   predicted class=B1  expected loss=0.4102564  P(node) =0.0002838397
##     class counts:    46    19    11     2     0
##    probabilities: 0.590 0.244 0.141 0.026 0.000 
## 
## Node number 111057: 39 observations
##   predicted class=B2  expected loss=0.5128205  P(node) =0.0001419198
##     class counts:    14    19     4     1     1
##    probabilities: 0.359 0.487 0.103 0.026 0.026 
## 
## Node number 111058: 72 observations
##   predicted class=B1  expected loss=0.4861111  P(node) =0.0002620059
##     class counts:    37    29     5     1     0
##    probabilities: 0.514 0.403 0.069 0.014 0.000 
## 
## Node number 111059: 154 observations
##   predicted class=B2  expected loss=0.5519481  P(node) =0.0005604015
##     class counts:    55    69    17    12     1
##    probabilities: 0.357 0.448 0.110 0.078 0.006 
## 
## Node number 112008: 46 observations
##   predicted class=B1  expected loss=0.4347826  P(node) =0.0001673926
##     class counts:    26    10     8     2     0
##    probabilities: 0.565 0.217 0.174 0.043 0.000 
## 
## Node number 112009: 78 observations
##   predicted class=B2  expected loss=0.525641  P(node) =0.0002838397
##     class counts:    31    37     8     2     0
##    probabilities: 0.397 0.474 0.103 0.026 0.000 
## 
## Node number 112014: 40 observations
##   predicted class=B1  expected loss=0.6  P(node) =0.0001455588
##     class counts:    16    12    11     1     0
##    probabilities: 0.400 0.300 0.275 0.025 0.000 
## 
## Node number 112015: 26 observations
##   predicted class=B3  expected loss=0.4230769  P(node) =9.461323e-05
##     class counts:     8     2    15     1     0
##    probabilities: 0.308 0.077 0.577 0.038 0.000 
## 
## Node number 113434: 78 observations
##   predicted class=B1  expected loss=0.5512821  P(node) =0.0002838397
##     class counts:    35    23    15     5     0
##    probabilities: 0.449 0.295 0.192 0.064 0.000 
## 
## Node number 113435: 244 observations
##   predicted class=B2  expected loss=0.5614754  P(node) =0.0008879088
##     class counts:    91   107    26    20     0
##    probabilities: 0.373 0.439 0.107 0.082 0.000 
## 
## Node number 123232: 209 observations
##   predicted class=B1  expected loss=0.507177  P(node) =0.0007605448
##     class counts:   103    70    22    14     0
##    probabilities: 0.493 0.335 0.105 0.067 0.000 
## 
## Node number 123233: 36 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.5277778  P(node) =0.0001310029
##     class counts:    11    17     5     2     1
##    probabilities: 0.306 0.472 0.139 0.056 0.028 
##   left son=246466 (15 obs) right son=246467 (21 obs)
##   Primary splits:
##       age               < 74.5   to the left,  improve=5.3968250, (0 missing)
##       reimbursement2008 < 8705   to the right, improve=1.5053320, (0 missing)
##       copd              < 0.5    to the right, improve=0.3703704, (0 missing)
##       depression        < 0.5    to the right, improve=0.3527778, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=0.2972583, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 8460   to the left,  agree=0.611, adj=0.067, (0 split)
## 
## Node number 123568: 158 observations
##   predicted class=B1  expected loss=0.5  P(node) =0.0005749573
##     class counts:    79    55    15     8     1
##    probabilities: 0.500 0.348 0.095 0.051 0.006 
## 
## Node number 123569: 601 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5856905  P(node) =0.002187021
##     class counts:   249   248    79    25     0
##    probabilities: 0.414 0.413 0.131 0.042 0.000 
##   left son=247138 (592 obs) right son=247139 (9 obs)
##   Primary splits:
##       reimbursement2008 < 4295   to the left,  improve=3.0859230, (0 missing)
##       age               < 62.5   to the right, improve=0.8258296, (0 missing)
##       copd              < 0.5    to the left,  improve=0.2730143, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.1209200, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.1102521, (0 missing)
## 
## Node number 123570: 346 observations
##   predicted class=B2  expected loss=0.5202312  P(node) =0.001259084
##     class counts:   122   166    44    13     1
##    probabilities: 0.353 0.480 0.127 0.038 0.003 
## 
## Node number 123571: 373 observations,    complexity param=9.962695e-05
##   predicted class=B1  expected loss=0.5978552  P(node) =0.001357336
##     class counts:   150   139    61    21     2
##    probabilities: 0.402 0.373 0.164 0.056 0.005 
##   left son=247142 (124 obs) right son=247143 (249 obs)
##   Primary splits:
##       alzheimers        < 0.5    to the right, improve=1.9370400, (0 missing)
##       reimbursement2008 < 6045   to the right, improve=1.9317030, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=1.1351660, (0 missing)
##       age               < 68.5   to the left,  improve=0.9923350, (0 missing)
##       stroke            < 0.5    to the right, improve=0.8206414, (0 missing)
##   Surrogate splits:
##       age               < 64.5   to the left,  agree=0.673, adj=0.016, (0 split)
##       stroke            < 0.5    to the right, agree=0.673, adj=0.016, (0 split)
##       reimbursement2008 < 5845   to the left,  agree=0.670, adj=0.008, (0 split)
## 
## Node number 123618: 26 observations
##   predicted class=B1  expected loss=0.3846154  P(node) =9.461323e-05
##     class counts:    16     4     4     2     0
##    probabilities: 0.615 0.154 0.154 0.077 0.000 
## 
## Node number 123619: 74 observations
##   predicted class=B2  expected loss=0.4324324  P(node) =0.0002692838
##     class counts:    22    42     9     1     0
##    probabilities: 0.297 0.568 0.122 0.014 0.000 
## 
## Node number 123848: 92 observations
##   predicted class=B1  expected loss=0.5  P(node) =0.0003347853
##     class counts:    46    23    17     6     0
##    probabilities: 0.500 0.250 0.185 0.065 0.000 
## 
## Node number 123849: 169 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.6213018  P(node) =0.000614986
##     class counts:    62    64    27    16     0
##    probabilities: 0.367 0.379 0.160 0.095 0.000 
##   left son=247698 (109 obs) right son=247699 (60 obs)
##   Primary splits:
##       reimbursement2008 < 14485  to the right, improve=2.3703890, (0 missing)
##       age               < 77.5   to the right, improve=1.8205180, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=1.5605270, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.9473954, (0 missing)
##       stroke            < 0.5    to the right, improve=0.8779250, (0 missing)
## 
## Node number 127398: 201 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.6865672  P(node) =0.0007314331
##     class counts:    63    58    49    27     4
##    probabilities: 0.313 0.289 0.244 0.134 0.020 
##   left son=254796 (112 obs) right son=254797 (89 obs)
##   Primary splits:
##       reimbursement2008 < 12625  to the left,  improve=2.6465710, (0 missing)
##       age               < 72.5   to the right, improve=1.5701210, (0 missing)
##       heart.failure     < 0.5    to the left,  improve=1.5204340, (0 missing)
##       alzheimers        < 0.5    to the left,  improve=0.8281641, (0 missing)
##       stroke            < 0.5    to the right, improve=0.4454147, (0 missing)
##   Surrogate splits:
##       age < 67.5   to the right, agree=0.587, adj=0.067, (0 split)
## 
## Node number 127399: 39 observations
##   predicted class=B2  expected loss=0.6410256  P(node) =0.0001419198
##     class counts:     6    14    12     3     4
##    probabilities: 0.154 0.359 0.308 0.077 0.103 
## 
## Node number 130242: 89 observations
##   predicted class=B1  expected loss=0.5955056  P(node) =0.0003238684
##     class counts:    36    15    17    14     7
##    probabilities: 0.404 0.169 0.191 0.157 0.079 
## 
## Node number 130243: 52 observations
##   predicted class=B2  expected loss=0.6730769  P(node) =0.0001892265
##     class counts:    12    17     6    12     5
##    probabilities: 0.231 0.327 0.115 0.231 0.096 
## 
## Node number 246466: 15 observations
##   predicted class=B1  expected loss=0.4  P(node) =5.458456e-05
##     class counts:     9     2     3     0     1
##    probabilities: 0.600 0.133 0.200 0.000 0.067 
## 
## Node number 246467: 21 observations
##   predicted class=B2  expected loss=0.2857143  P(node) =7.641838e-05
##     class counts:     2    15     2     2     0
##    probabilities: 0.095 0.714 0.095 0.095 0.000 
## 
## Node number 247138: 592 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5810811  P(node) =0.002154271
##     class counts:   248   240    79    25     0
##    probabilities: 0.419 0.405 0.133 0.042 0.000 
##   left son=494276 (135 obs) right son=494277 (457 obs)
##   Primary splits:
##       age               < 82.5   to the right, improve=1.0162580, (0 missing)
##       reimbursement2008 < 3485   to the left,  improve=0.9533819, (0 missing)
##       copd              < 0.5    to the left,  improve=0.2603666, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.1489946, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.1384892, (0 missing)
## 
## Node number 247139: 9 observations
##   predicted class=B2  expected loss=0.1111111  P(node) =3.275073e-05
##     class counts:     1     8     0     0     0
##    probabilities: 0.111 0.889 0.000 0.000 0.000 
## 
## Node number 247142: 124 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.516129  P(node) =0.0004512323
##     class counts:    60    39    19     5     1
##    probabilities: 0.484 0.315 0.153 0.040 0.008 
##   left son=494284 (114 obs) right son=494285 (10 obs)
##   Primary splits:
##       reimbursement2008 < 8555   to the left,  improve=3.2894170, (0 missing)
##       age               < 62.5   to the right, improve=1.3134040, (0 missing)
##       osteoporosis      < 0.5    to the right, improve=0.8306452, (0 missing)
##       stroke            < 0.5    to the right, improve=0.6624062, (0 missing)
##       bucket2008        < 2.5    to the left,  improve=0.6169185, (0 missing)
## 
## Node number 247143: 249 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.5983936  P(node) =0.0009061036
##     class counts:    90   100    42    16     1
##    probabilities: 0.361 0.402 0.169 0.064 0.004 
##   left son=494286 (217 obs) right son=494287 (32 obs)
##   Primary splits:
##       reimbursement2008 < 6045   to the right, improve=2.8382200, (0 missing)
##       age               < 68.5   to the left,  improve=1.5757780, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.8580882, (0 missing)
##       copd              < 0.5    to the left,  improve=0.4427711, (0 missing)
##       stroke            < 0.5    to the right, improve=0.2244234, (0 missing)
## 
## Node number 247698: 109 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5779817  P(node) =0.0003966478
##     class counts:    46    34    19    10     0
##    probabilities: 0.422 0.312 0.174 0.092 0.000 
##   left son=495396 (72 obs) right son=495397 (37 obs)
##   Primary splits:
##       osteoporosis      < 0.5    to the left,  improve=2.1824740, (0 missing)
##       reimbursement2008 < 17235  to the right, improve=1.3957040, (0 missing)
##       age               < 70.5   to the left,  improve=1.2827700, (0 missing)
##       stroke            < 0.5    to the left,  improve=1.2406940, (0 missing)
##       bucket2008        < 3.5    to the left,  improve=0.2455781, (0 missing)
##   Surrogate splits:
##       reimbursement2008 < 14660  to the right, agree=0.679, adj=0.054, (0 split)
## 
## Node number 247699: 60 observations
##   predicted class=B2  expected loss=0.5  P(node) =0.0002183382
##     class counts:    16    30     8     6     0
##    probabilities: 0.267 0.500 0.133 0.100 0.000 
## 
## Node number 254796: 112 observations
##   predicted class=B1  expected loss=0.6160714  P(node) =0.0004075647
##     class counts:    43    27    31    10     1
##    probabilities: 0.384 0.241 0.277 0.089 0.009 
## 
## Node number 254797: 89 observations
##   predicted class=B2  expected loss=0.6516854  P(node) =0.0003238684
##     class counts:    20    31    18    17     3
##    probabilities: 0.225 0.348 0.202 0.191 0.034 
## 
## Node number 494276: 135 observations
##   predicted class=B1  expected loss=0.5259259  P(node) =0.000491261
##     class counts:    64    49    20     2     0
##    probabilities: 0.474 0.363 0.148 0.015 0.000 
## 
## Node number 494277: 457 observations,    complexity param=6.641797e-05
##   predicted class=B2  expected loss=0.5820569  P(node) =0.00166301
##     class counts:   184   191    59    23     0
##    probabilities: 0.403 0.418 0.129 0.050 0.000 
##   left son=988554 (290 obs) right son=988555 (167 obs)
##   Primary splits:
##       age               < 74.5   to the left,  improve=0.9874503, (0 missing)
##       reimbursement2008 < 3495   to the left,  improve=0.9861916, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.3272674, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.2337493, (0 missing)
##       copd              < 0.5    to the left,  improve=0.1994562, (0 missing)
##   Surrogate splits:
##       alzheimers < 0.5    to the left,  agree=0.637, adj=0.006, (0 split)
## 
## Node number 494284: 114 observations
##   predicted class=B1  expected loss=0.4824561  P(node) =0.0004148426
##     class counts:    59    32    18     4     1
##    probabilities: 0.518 0.281 0.158 0.035 0.009 
## 
## Node number 494285: 10 observations
##   predicted class=B2  expected loss=0.3  P(node) =3.63897e-05
##     class counts:     1     7     1     1     0
##    probabilities: 0.100 0.700 0.100 0.100 0.000 
## 
## Node number 494286: 217 observations
##   predicted class=B2  expected loss=0.5714286  P(node) =0.0007896566
##     class counts:    78    93    30    15     1
##    probabilities: 0.359 0.429 0.138 0.069 0.005 
## 
## Node number 494287: 32 observations,    complexity param=5.534831e-05
##   predicted class=B1  expected loss=0.625  P(node) =0.0001164471
##     class counts:    12     7    12     1     0
##    probabilities: 0.375 0.219 0.375 0.031 0.000 
##   left son=988574 (11 obs) right son=988575 (21 obs)
##   Primary splits:
##       age               < 72.5   to the left,  improve=1.8097940, (0 missing)
##       reimbursement2008 < 5975   to the left,  improve=0.7232143, (0 missing)
##       copd              < 0.5    to the left,  improve=0.6875000, (0 missing)
## 
## Node number 495396: 72 observations
##   predicted class=B1  expected loss=0.5138889  P(node) =0.0002620059
##     class counts:    35    17    12     8     0
##    probabilities: 0.486 0.236 0.167 0.111 0.000 
## 
## Node number 495397: 37 observations
##   predicted class=B2  expected loss=0.5405405  P(node) =0.0001346419
##     class counts:    11    17     7     2     0
##    probabilities: 0.297 0.459 0.189 0.054 0.000 
## 
## Node number 988554: 290 observations,    complexity param=6.641797e-05
##   predicted class=B1  expected loss=0.5724138  P(node) =0.001055301
##     class counts:   124   114    37    15     0
##    probabilities: 0.428 0.393 0.128 0.052 0.000 
##   left son=1977108 (234 obs) right son=1977109 (56 obs)
##   Primary splits:
##       age               < 62.5   to the right, improve=1.0825800, (0 missing)
##       reimbursement2008 < 3945   to the right, improve=0.7040408, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.6026089, (0 missing)
##       stroke            < 0.5    to the left,  improve=0.2655768, (0 missing)
##       copd              < 0.5    to the left,  improve=0.1804923, (0 missing)
## 
## Node number 988555: 167 observations,    complexity param=5.534831e-05
##   predicted class=B2  expected loss=0.5389222  P(node) =0.0006077081
##     class counts:    60    77    22     8     0
##    probabilities: 0.359 0.461 0.132 0.048 0.000 
##   left son=1977110 (39 obs) right son=1977111 (128 obs)
##   Primary splits:
##       reimbursement2008 < 4105   to the right, improve=1.3886510, (0 missing)
##       osteoporosis      < 0.5    to the left,  improve=0.7439669, (0 missing)
##       age               < 81.5   to the left,  improve=0.4824922, (0 missing)
##       alzheimers        < 0.5    to the right, improve=0.2060442, (0 missing)
##       copd              < 0.5    to the left,  improve=0.1297289, (0 missing)
## 
## Node number 988574: 11 observations
##   predicted class=B1  expected loss=0.3636364  P(node) =4.002868e-05
##     class counts:     7     2     2     0     0
##    probabilities: 0.636 0.182 0.182 0.000 0.000 
## 
## Node number 988575: 21 observations
##   predicted class=B3  expected loss=0.5238095  P(node) =7.641838e-05
##     class counts:     5     5    10     1     0
##    probabilities: 0.238 0.238 0.476 0.048 0.000 
## 
## Node number 1977108: 234 observations
##   predicted class=B1  expected loss=0.5470085  P(node) =0.0008515191
##     class counts:   106    89    28    11     0
##    probabilities: 0.453 0.380 0.120 0.047 0.000 
## 
## Node number 1977109: 56 observations
##   predicted class=B2  expected loss=0.5535714  P(node) =0.0002037823
##     class counts:    18    25     9     4     0
##    probabilities: 0.321 0.446 0.161 0.071 0.000 
## 
## Node number 1977110: 39 observations
##   predicted class=B1  expected loss=0.5128205  P(node) =0.0001419198
##     class counts:    19    14     5     1     0
##    probabilities: 0.487 0.359 0.128 0.026 0.000 
## 
## Node number 1977111: 128 observations
##   predicted class=B2  expected loss=0.5078125  P(node) =0.0004657882
##     class counts:    41    63    17     7     0
##    probabilities: 0.320 0.492 0.133 0.055 0.000 
## 
## n= 274803 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
##       1) root 274803 90337 B1 (0.67 0.19 0.089 0.043 0.0058)  
##         2) reimbursement2008< 1565 165987 20938 B1 (0.87 0.074 0.037 0.014 0.0014) *
##         3) reimbursement2008>=1565 108816 68841 B2 (0.36 0.37 0.17 0.088 0.012)  
##           6) reimbursement2008< 3065 39298 18853 B1 (0.52 0.31 0.12 0.045 0.0046)  
##            12) reimbursement2008< 2175 20077  8527 B1 (0.58 0.27 0.11 0.042 0.0038)  
##              24) diabetes< 0.5 8826  3280 B1 (0.63 0.24 0.091 0.035 0.0029) *
##              25) diabetes>=0.5 11251  5247 B1 (0.53 0.29 0.12 0.046 0.0045)  
##                50) kidney< 0.5 9007  4045 B1 (0.55 0.28 0.12 0.042 0.0044)  
##                 100) reimbursement2008< 1875 4935  2071 B1 (0.58 0.26 0.11 0.042 0.0045) *
##                 101) reimbursement2008>=1875 4072  1974 B1 (0.52 0.31 0.13 0.042 0.0044)  
##                   202) cancer< 0.5 3786  1807 B1 (0.52 0.3 0.13 0.041 0.0048) *
##                   203) cancer>=0.5 286   167 B1 (0.42 0.4 0.13 0.056 0)  
##                     406) age< 73.5 128    64 B1 (0.5 0.33 0.11 0.062 0)  
##                       812) depression< 0.5 95    41 B1 (0.57 0.26 0.12 0.053 0) *
##                       813) depression>=0.5 33    16 B2 (0.3 0.52 0.091 0.091 0) *
##                     407) age>=73.5 158    85 B2 (0.35 0.46 0.14 0.051 0) *
##                51) kidney>=0.5 2244  1202 B1 (0.46 0.33 0.14 0.064 0.0049)  
##                 102) heart.failure< 0.5 992   477 B1 (0.52 0.29 0.13 0.057 0.002) *
##                 103) heart.failure>=0.5 1252   725 B1 (0.42 0.36 0.15 0.069 0.0072)  
##                   206) arthritis< 0.5 904   486 B1 (0.46 0.34 0.13 0.063 0.0066)  
##                     412) reimbursement2008< 1735 270   125 B1 (0.54 0.27 0.13 0.056 0.0037) *
##                     413) reimbursement2008>=1735 634   361 B1 (0.43 0.36 0.13 0.066 0.0079)  
##                       826) age< 91.5 596   330 B1 (0.45 0.36 0.13 0.057 0.0084)  
##                        1652) reimbursement2008>=1765 555   302 B1 (0.46 0.35 0.14 0.052 0.0072)  
##                          3304) reimbursement2008< 1955 265   130 B1 (0.51 0.31 0.13 0.049 0.0038)  
##                            6608) stroke< 0.5 251   118 B1 (0.53 0.29 0.13 0.044 0.004)  
##                             13216) cancer< 0.5 235   106 B1 (0.55 0.27 0.13 0.047 0.0043) *
##                             13217) cancer>=0.5 16     7 B2 (0.25 0.56 0.19 0 0) *
##                            6609) stroke>=0.5 14     5 B2 (0.14 0.64 0.071 0.14 0) *
##                          3305) reimbursement2008>=1955 290   172 B1 (0.41 0.38 0.14 0.055 0.01)  
##                            6610) age< 81.5 213   120 B1 (0.44 0.35 0.14 0.07 0.0047)  
##                             13220) age>=44.5 201   110 B1 (0.45 0.33 0.14 0.07 0) *
##                             13221) age< 44.5 12     5 B2 (0.17 0.58 0.083 0.083 0.083) *
##                            6611) age>=81.5 77    40 B2 (0.32 0.48 0.16 0.013 0.026) *
##                        1653) reimbursement2008< 1765 41    20 B2 (0.32 0.51 0.024 0.12 0.024) *
##                       827) age>=91.5 38    21 B2 (0.18 0.45 0.16 0.21 0) *
##                   207) arthritis>=0.5 348   205 B2 (0.31 0.41 0.18 0.086 0.0086) *
##            13) reimbursement2008>=2175 19221 10326 B1 (0.46 0.35 0.13 0.049 0.0054)  
##              26) diabetes< 0.5 7137  3360 B1 (0.53 0.31 0.11 0.042 0.0046)  
##                52) arthritis< 0.5 5554  2471 B1 (0.56 0.3 0.1 0.039 0.0049)  
##                 104) ihd< 0.5 2348   933 B1 (0.6 0.27 0.092 0.031 0.0051) *
##                 105) ihd>=0.5 3206  1538 B1 (0.52 0.32 0.11 0.045 0.0047)  
##                   210) depression< 0.5 2325  1056 B1 (0.55 0.3 0.11 0.043 0.0052) *
##                   211) depression>=0.5 881   482 B1 (0.45 0.36 0.13 0.052 0.0034)  
##                     422) kidney< 0.5 763   405 B1 (0.47 0.34 0.13 0.052 0.0039) *
##                     423) kidney>=0.5 118    63 B2 (0.35 0.47 0.14 0.051 0)  
##                       846) reimbursement2008>=2865 22    10 B1 (0.55 0.23 0.18 0.045 0) *
##                       847) reimbursement2008< 2865 96    46 B2 (0.3 0.52 0.12 0.052 0) *
##                53) arthritis>=0.5 1583   889 B1 (0.44 0.37 0.14 0.052 0.0038)  
##                 106) stroke< 0.5 1525   844 B1 (0.45 0.36 0.13 0.054 0.0039)  
##                   212) cancer< 0.5 1438   784 B1 (0.45 0.36 0.13 0.053 0.0042)  
##                     424) reimbursement2008>=2715 495   280 B1 (0.43 0.41 0.1 0.053 0.004)  
##                       848) reimbursement2008>=2795 385   210 B1 (0.45 0.38 0.1 0.06 0.0052)  
##                        1696) age< 80.5 263   131 B1 (0.5 0.36 0.099 0.042 0) *
##                        1697) age>=80.5 122    70 B2 (0.35 0.43 0.11 0.098 0.016) *
##                       849) reimbursement2008< 2795 110    54 B2 (0.36 0.51 0.1 0.027 0) *
##                     425) reimbursement2008< 2715 943   504 B1 (0.47 0.33 0.15 0.053 0.0042) *
##                   213) cancer>=0.5 87    48 B2 (0.31 0.45 0.17 0.069 0) *
##                 107) stroke>=0.5 58    26 B2 (0.22 0.55 0.21 0.017 0) *
##              27) diabetes>=0.5 12084  6966 B1 (0.42 0.37 0.15 0.054 0.0059)  
##                54) arthritis< 0.5 8413  4653 B1 (0.45 0.35 0.15 0.052 0.0056)  
##                 108) heart.failure< 0.5 4375  2220 B1 (0.49 0.34 0.13 0.039 0.0039)  
##                   216) cancer< 0.5 3992  1978 B1 (0.5 0.33 0.12 0.038 0.0033)  
##                     432) ihd< 0.5 1265   562 B1 (0.56 0.29 0.12 0.035 0.0032) *
##                     433) ihd>=0.5 2727  1416 B1 (0.48 0.35 0.13 0.04 0.0033)  
##                       866) reimbursement2008< 2615 1499   736 B1 (0.51 0.33 0.13 0.035 0.002) *
##                       867) reimbursement2008>=2615 1228   680 B1 (0.45 0.37 0.13 0.046 0.0049)  
##                        1734) reimbursement2008>=2995 171    81 B1 (0.53 0.27 0.14 0.058 0.0058) *
##                        1735) reimbursement2008< 2995 1057   599 B1 (0.43 0.39 0.13 0.044 0.0047)  
##                          3470) age< 83.5 840   458 B1 (0.45 0.37 0.12 0.049 0.006)  
##                            6940) age< 54.5 71    31 B1 (0.56 0.23 0.15 0.042 0.014) *
##                            6941) age>=54.5 769   427 B1 (0.44 0.38 0.12 0.049 0.0052)  
##                             13882) age>=70.5 472   249 B1 (0.47 0.35 0.12 0.053 0.0085)  
##                               27764) age>=73.5 343   191 B1 (0.44 0.4 0.11 0.047 0.0058)  
##                                 55528) reimbursement2008>=2835 117    57 B1 (0.51 0.32 0.13 0.026 0.0085)  
##                                  111056) reimbursement2008< 2945 78    32 B1 (0.59 0.24 0.14 0.026 0) *
##                                  111057) reimbursement2008>=2945 39    20 B2 (0.36 0.49 0.1 0.026 0.026) *
##                                 55529) reimbursement2008< 2835 226   128 B2 (0.41 0.43 0.097 0.058 0.0044)  
##                                  111058) age>=80.5 72    35 B1 (0.51 0.4 0.069 0.014 0) *
##                                  111059) age< 80.5 154    85 B2 (0.36 0.45 0.11 0.078 0.0065) *
##                               27765) age< 73.5 129    58 B1 (0.55 0.21 0.16 0.07 0.016) *
##                             13883) age< 70.5 297   167 B2 (0.4 0.44 0.12 0.044 0)  
##                               27766) osteoporosis< 0.5 218   125 B1 (0.43 0.41 0.13 0.037 0)  
##                                 55532) reimbursement2008< 2945 194   108 B1 (0.44 0.38 0.14 0.036 0) *
##                                 55533) reimbursement2008>=2945 24     9 B2 (0.29 0.62 0.042 0.042 0) *
##                               27767) osteoporosis>=0.5 79    38 B2 (0.33 0.52 0.089 0.063 0) *
##                          3471) age>=83.5 217   116 B2 (0.35 0.47 0.16 0.028 0) *
##                   217) cancer>=0.5 383   220 B2 (0.37 0.43 0.15 0.044 0.01)  
##                     434) reimbursement2008< 2705 238   136 B1 (0.43 0.36 0.16 0.038 0.013)  
##                       868) depression< 0.5 167    84 B1 (0.5 0.3 0.15 0.042 0.012) *
##                       869) depression>=0.5 71    35 B2 (0.27 0.51 0.18 0.028 0.014) *
##                     435) reimbursement2008>=2705 145    68 B2 (0.27 0.53 0.14 0.055 0.0069) *
##                 109) heart.failure>=0.5 4038  2433 B1 (0.4 0.36 0.17 0.066 0.0074)  
##                   218) kidney< 0.5 2819  1620 B1 (0.43 0.35 0.16 0.065 0.0064)  
##                     436) ihd< 0.5 635   319 B1 (0.5 0.31 0.15 0.041 0.0063) *
##                     437) ihd>=0.5 2184  1301 B1 (0.4 0.36 0.16 0.072 0.0064)  
##                       874) reimbursement2008< 2315 393   202 B1 (0.49 0.34 0.12 0.051 0.0051) *
##                       875) reimbursement2008>=2315 1791  1099 B1 (0.39 0.36 0.17 0.076 0.0067)  
##                        1750) age>=39.5 1752  1066 B1 (0.39 0.36 0.17 0.075 0.0068)  
##                          3500) depression< 0.5 1099   639 B1 (0.42 0.35 0.15 0.069 0.0064)  
##                            7000) age< 95.5 1074   620 B1 (0.42 0.35 0.15 0.069 0.0065)  
##                             14000) copd< 0.5 808   450 B1 (0.44 0.34 0.14 0.075 0.0062) *
##                             14001) copd>=0.5 266   166 B2 (0.36 0.38 0.21 0.049 0.0075)  
##                               28002) reimbursement2008>=2540 192   114 B2 (0.38 0.41 0.15 0.057 0.01)  
##                                 56004) age< 78.5 124    67 B1 (0.46 0.38 0.13 0.032 0)  
##                                  112008) age>=72.5 46    20 B1 (0.57 0.22 0.17 0.043 0) *
##                                  112009) age< 72.5 78    41 B2 (0.4 0.47 0.1 0.026 0) *
##                                 56005) age>=78.5 68    37 B2 (0.22 0.46 0.19 0.1 0.029) *
##                               28003) reimbursement2008< 2540 74    48 B3 (0.32 0.3 0.35 0.027 0)  
##                                 56006) cancer>=0.5 8     0 B2 (0 1 0 0 0) *
##                                 56007) cancer< 0.5 66    40 B3 (0.36 0.21 0.39 0.03 0)  
##                                  112014) alzheimers< 0.5 40    24 B1 (0.4 0.3 0.27 0.025 0) *
##                                  112015) alzheimers>=0.5 26    11 B3 (0.31 0.077 0.58 0.038 0) *
##                            7001) age>=95.5 25    10 B2 (0.24 0.6 0.08 0.08 0) *
##                          3501) depression>=0.5 653   412 B2 (0.35 0.37 0.19 0.084 0.0077)  
##                            7002) reimbursement2008< 2655 303   183 B1 (0.4 0.33 0.2 0.069 0.0033) *
##                            7003) reimbursement2008>=2655 350   208 B2 (0.3 0.41 0.18 0.097 0.011) *
##                        1751) age< 39.5 39    18 B2 (0.15 0.54 0.15 0.15 0) *
##                   219) kidney>=0.5 1219   734 B2 (0.33 0.4 0.19 0.07 0.0098)  
##                     438) reimbursement2008< 2615 613   379 B1 (0.38 0.37 0.18 0.059 0.0098)  
##                       876) osteoporosis>=0.5 180   102 B1 (0.43 0.38 0.13 0.061 0)  
##                        1752) reimbursement2008< 2455 112    56 B1 (0.5 0.29 0.12 0.08 0) *
##                        1753) reimbursement2008>=2455 68    33 B2 (0.32 0.51 0.13 0.029 0) *
##                       877) osteoporosis< 0.5 433   275 B2 (0.36 0.36 0.2 0.058 0.014)  
##                        1754) stroke< 0.5 403   252 B1 (0.37 0.36 0.2 0.05 0.015)  
##                          3508) reimbursement2008< 2585 382   238 B2 (0.37 0.38 0.19 0.047 0.01)  
##                            7016) depression< 0.5 229   136 B1 (0.41 0.36 0.19 0.035 0.0087)  
##                             14032) cancer>=0.5 15     5 B1 (0.67 0.13 0.2 0 0) *
##                             14033) cancer< 0.5 214   131 B1 (0.39 0.37 0.19 0.037 0.0093)  
##                               28066) reimbursement2008< 2515 169    98 B1 (0.42 0.34 0.19 0.036 0.012) *
##                               28067) reimbursement2008>=2515 45    23 B2 (0.27 0.49 0.2 0.044 0) *
##                            7017) depression>=0.5 153    91 B2 (0.32 0.41 0.2 0.065 0.013)  
##                             14034) reimbursement2008>=2545 14     5 B1 (0.64 0.14 0.14 0.071 0) *
##                             14035) reimbursement2008< 2545 139    79 B2 (0.29 0.43 0.2 0.065 0.014) *
##                          3509) reimbursement2008>=2585 21    12 B1 (0.43 0.14 0.24 0.095 0.095) *
##                        1755) stroke>=0.5 30    19 B2 (0.17 0.37 0.3 0.17 0) *
##                     439) reimbursement2008>=2615 606   347 B2 (0.28 0.43 0.2 0.081 0.0099) *
##                55) arthritis>=0.5 3671  2129 B2 (0.37 0.42 0.15 0.057 0.0065)  
##                 110) reimbursement2008< 2665 2068  1224 B1 (0.41 0.4 0.14 0.057 0.0048)  
##                   220) ihd< 0.5 517   274 B1 (0.47 0.37 0.11 0.048 0.0019)  
##                     440) reimbursement2008< 2295 143    57 B1 (0.6 0.26 0.077 0.063 0) *
##                     441) reimbursement2008>=2295 374   217 B1 (0.42 0.41 0.12 0.043 0.0027)  
##                       882) reimbursement2008< 2315 25     6 B2 (0.24 0.76 0 0 0) *
##                       883) reimbursement2008>=2315 349   198 B1 (0.43 0.39 0.13 0.046 0.0029)  
##                        1766) cancer< 0.5 336   186 B1 (0.45 0.38 0.13 0.042 0)  
##                          3532) age< 90.5 322   176 B1 (0.45 0.37 0.13 0.043 0) *
##                          3533) age>=90.5 14     5 B2 (0.29 0.64 0.071 0 0) *
##                        1767) cancer>=0.5 13     6 B2 (0.077 0.54 0.15 0.15 0.077) *
##                   221) ihd>=0.5 1551   925 B2 (0.39 0.4 0.14 0.059 0.0058)  
##                     442) age< 35 18     5 B1 (0.72 0.22 0 0.056 0) *
##                     443) age>=35 1533   911 B2 (0.38 0.41 0.15 0.059 0.0059)  
##                       886) kidney< 0.5 1101   656 B1 (0.4 0.4 0.14 0.052 0.0045)  
##                        1772) stroke< 0.5 1057   623 B1 (0.41 0.4 0.14 0.051 0.0038)  
##                          3544) cancer< 0.5 1008   590 B1 (0.41 0.4 0.13 0.053 0.004)  
##                            7088) reimbursement2008>=2535 275   154 B2 (0.39 0.44 0.12 0.04 0.0036)  
##                             14176) age< 63.5 44    17 B2 (0.32 0.61 0.045 0.023 0) *
##                             14177) age>=63.5 231   137 B1 (0.41 0.41 0.14 0.043 0.0043)  
##                               28354) alzheimers< 0.5 169    95 B1 (0.44 0.36 0.15 0.047 0.0059) *
##                               28355) alzheimers>=0.5 62    28 B2 (0.32 0.55 0.097 0.032 0) *
##                            7089) reimbursement2008< 2535 733   423 B1 (0.42 0.38 0.14 0.057 0.0041)  
##                             14178) age>=97.5 10     3 B1 (0.7 0.2 0.1 0 0) *
##                             14179) age< 97.5 723   420 B1 (0.42 0.38 0.14 0.058 0.0041)  
##                               28358) age< 90.5 689   394 B1 (0.43 0.38 0.13 0.055 0.0044)  
##                                 56716) heart.failure< 0.5 367   198 B1 (0.46 0.36 0.14 0.035 0.0082) *
##                                 56717) heart.failure>=0.5 322   192 B2 (0.39 0.4 0.13 0.078 0)  
##                                  113434) age< 67.5 78    43 B1 (0.45 0.29 0.19 0.064 0) *
##                                  113435) age>=67.5 244   137 B2 (0.37 0.44 0.11 0.082 0) *
##                               28359) age>=90.5 34    18 B2 (0.24 0.47 0.18 0.12 0) *
##                          3545) cancer>=0.5 49    29 B2 (0.33 0.41 0.24 0.02 0) *
##                        1773) stroke>=0.5 44    20 B2 (0.25 0.55 0.11 0.068 0.023) *
##                       887) kidney>=0.5 432   254 B2 (0.33 0.41 0.17 0.079 0.0093)  
##                        1774) reimbursement2008>=2215 403   232 B2 (0.32 0.42 0.17 0.069 0.0099) *
##                        1775) reimbursement2008< 2215 29    16 B1 (0.45 0.24 0.1 0.21 0) *
##                 111) reimbursement2008>=2665 1603   878 B2 (0.32 0.45 0.16 0.058 0.0087) *
##           7) reimbursement2008>=3065 69518 41677 B2 (0.27 0.4 0.2 0.11 0.017)  
##            14) diabetes< 0.5 15717  8966 B1 (0.43 0.35 0.15 0.064 0.0071)  
##              28) cancer< 0.5 13123  7034 B1 (0.46 0.34 0.13 0.058 0.0065)  
##                56) arthritis< 0.5 9625  4692 B1 (0.51 0.31 0.12 0.054 0.0058)  
##                 112) ihd< 0.5 3135  1246 B1 (0.6 0.26 0.095 0.036 0.0032)  
##                   224) depression< 0.5 2292   821 B1 (0.64 0.24 0.08 0.034 0.0044) *
##                   225) depression>=0.5 843   425 B1 (0.5 0.33 0.14 0.04 0)  
##                     450) age< 92.5 810   398 B1 (0.51 0.32 0.13 0.041 0)  
##                       900) reimbursement2008>=11525 117    40 B1 (0.66 0.21 0.068 0.06 0) *
##                       901) reimbursement2008< 11525 693   358 B1 (0.48 0.33 0.14 0.038 0)  
##                        1802) reimbursement2008< 11105 684   352 B1 (0.49 0.34 0.14 0.038 0)  
##                          3604) reimbursement2008< 4365 286   134 B1 (0.53 0.33 0.12 0.017 0) *
##                          3605) reimbursement2008>=4365 398   218 B1 (0.45 0.34 0.15 0.053 0)  
##                            7210) reimbursement2008>=4700 340   173 B1 (0.49 0.31 0.14 0.053 0) *
##                            7211) reimbursement2008< 4700 58    28 B2 (0.22 0.52 0.21 0.052 0) *
##                        1803) reimbursement2008>=11105 9     4 B3 (0.33 0.11 0.56 0 0) *
##                     451) age>=92.5 33    14 B2 (0.18 0.58 0.21 0.03 0) *
##                 113) ihd>=0.5 6490  3446 B1 (0.47 0.33 0.13 0.063 0.0071)  
##                   226) depression< 0.5 4266  2110 B1 (0.51 0.31 0.12 0.056 0.0061)  
##                     452) osteoporosis< 0.5 3304  1572 B1 (0.52 0.3 0.12 0.055 0.0064)  
##                       904) reimbursement2008>=5905 1626   714 B1 (0.56 0.25 0.12 0.061 0.0068) *
##                       905) reimbursement2008< 5905 1678   858 B1 (0.49 0.34 0.12 0.05 0.006)  
##                        1810) reimbursement2008< 5695 1608   814 B1 (0.49 0.33 0.12 0.051 0.0062) *
##                        1811) reimbursement2008>=5695 70    34 B2 (0.37 0.51 0.086 0.029 0) *
##                     453) osteoporosis>=0.5 962   538 B1 (0.44 0.38 0.12 0.057 0.0052)  
##                       906) stroke< 0.5 857   465 B1 (0.46 0.37 0.12 0.056 0.0047)  
##                        1812) heart.failure< 0.5 405   203 B1 (0.5 0.35 0.1 0.044 0.0074)  
##                          3624) age< 83.5 329   159 B1 (0.52 0.32 0.11 0.049 0.0091) *
##                          3625) age>=83.5 76    41 B2 (0.42 0.46 0.092 0.026 0)  
##                            7250) reimbursement2008>=6785 21     7 B1 (0.67 0.24 0.095 0 0) *
##                            7251) reimbursement2008< 6785 55    25 B2 (0.33 0.55 0.091 0.036 0) *
##                        1813) heart.failure>=0.5 452   262 B1 (0.42 0.38 0.13 0.066 0.0022)  
##                          3626) reimbursement2008>=3875 362   201 B1 (0.44 0.35 0.13 0.069 0.0028) *
##                          3627) reimbursement2008< 3875 90    45 B2 (0.32 0.5 0.12 0.056 0)  
##                            7254) age< 69.5 21     9 B1 (0.57 0.29 0.048 0.095 0) *
##                            7255) age>=69.5 69    30 B2 (0.25 0.57 0.14 0.043 0) *
##                       907) stroke>=0.5 105    54 B2 (0.3 0.49 0.13 0.067 0.0095) *
##                   227) depression>=0.5 2224  1336 B1 (0.4 0.35 0.16 0.076 0.009)  
##                     454) kidney< 0.5 1518   863 B1 (0.43 0.34 0.16 0.061 0.0053) *
##                     455) kidney>=0.5 706   440 B2 (0.33 0.38 0.17 0.11 0.017)  
##                       910) reimbursement2008>=3155 696   431 B2 (0.33 0.38 0.16 0.11 0.017)  
##                        1820) heart.failure< 0.5 177    99 B1 (0.44 0.35 0.15 0.062 0) *
##                        1821) heart.failure>=0.5 519   316 B2 (0.3 0.39 0.17 0.12 0.023) *
##                       911) reimbursement2008< 3155 10     4 B3 (0.1 0.1 0.6 0.2 0) *
##                57) arthritis>=0.5 3498  2017 B2 (0.33 0.42 0.17 0.069 0.0083)  
##                 114) reimbursement2008< 8525 2340  1270 B2 (0.31 0.46 0.17 0.062 0.0064)  
##                   228) reimbursement2008< 4645 1359   754 B2 (0.34 0.45 0.15 0.056 0.0059)  
##                     456) ihd< 0.5 440   248 B2 (0.4 0.44 0.11 0.045 0.0045)  
##                       912) reimbursement2008< 3155 58    22 B2 (0.34 0.62 0 0.017 0.017) *
##                       913) reimbursement2008>=3155 382   225 B1 (0.41 0.41 0.13 0.05 0.0026)  
##                        1826) reimbursement2008< 3245 25     8 B1 (0.68 0.28 0.04 0 0) *
##                        1827) reimbursement2008>=3245 357   208 B2 (0.39 0.42 0.13 0.053 0.0028)  
##                          3654) age>=80.5 91    46 B1 (0.49 0.34 0.099 0.066 0) *
##                          3655) age< 80.5 266   148 B2 (0.36 0.44 0.15 0.049 0.0038) *
##                     457) ihd>=0.5 919   506 B2 (0.32 0.45 0.17 0.061 0.0065) *
##                   229) reimbursement2008>=4645 981   516 B2 (0.26 0.47 0.19 0.069 0.0071) *
##                 115) reimbursement2008>=8525 1158   722 B1 (0.38 0.35 0.17 0.085 0.012)  
##                   230) copd< 0.5 714   396 B1 (0.45 0.33 0.13 0.085 0.007)  
##                     460) depression< 0.5 412   196 B1 (0.52 0.29 0.1 0.073 0.0097) *
##                     461) depression>=0.5 302   183 B2 (0.34 0.39 0.16 0.1 0.0033)  
##                       922) age>=92.5 9     3 B1 (0.67 0 0.11 0.11 0.11) *
##                       923) age< 92.5 293   174 B2 (0.33 0.41 0.16 0.1 0)  
##                        1846) stroke>=0.5 39    19 B1 (0.51 0.31 0.1 0.077 0) *
##                        1847) stroke< 0.5 254   147 B2 (0.3 0.42 0.17 0.11 0) *
##                   231) copd>=0.5 444   272 B2 (0.27 0.39 0.24 0.086 0.02)  
##                     462) osteoporosis< 0.5 282   187 B2 (0.31 0.34 0.25 0.082 0.018)  
##                       924) reimbursement2008< 27390 220   143 B1 (0.35 0.3 0.26 0.073 0.018)  
##                        1848) reimbursement2008>=12810 132    78 B1 (0.41 0.32 0.2 0.068 0.0076)  
##                          3696) age< 84.5 105    55 B1 (0.48 0.33 0.15 0.029 0.0095) *
##                          3697) age>=84.5 27    17 B3 (0.15 0.26 0.37 0.22 0) *
##                        1849) reimbursement2008< 12810 88    57 B3 (0.26 0.27 0.35 0.08 0.034) *
##                       925) reimbursement2008>=27390 62    33 B2 (0.18 0.47 0.23 0.11 0.016) *
##                     463) osteoporosis>=0.5 162    85 B2 (0.19 0.48 0.22 0.093 0.025) *
##              29) cancer>=0.5 2594  1539 B2 (0.26 0.41 0.24 0.091 0.01)  
##                58) reimbursement2008< 5770 1000   562 B2 (0.3 0.44 0.19 0.07 0.005) *
##                59) reimbursement2008>=5770 1594   977 B2 (0.23 0.39 0.27 0.1 0.014)  
##                 118) reimbursement2008>=8645 1054   656 B2 (0.27 0.38 0.24 0.1 0.015)  
##                   236) arthritis< 0.5 745   464 B2 (0.31 0.38 0.2 0.097 0.013)  
##                     472) ihd< 0.5 159    94 B1 (0.41 0.32 0.21 0.05 0.013)  
##                       944) reimbursement2008>=11995 76    36 B1 (0.53 0.24 0.16 0.066 0.013) *
##                       945) reimbursement2008< 11995 83    50 B2 (0.3 0.4 0.25 0.036 0.012) *
##                     473) ihd>=0.5 586   356 B2 (0.28 0.39 0.2 0.11 0.014) *
##                   237) arthritis>=0.5 309   192 B2 (0.16 0.38 0.32 0.12 0.019)  
##                     474) reimbursement2008>=10960 237   136 B2 (0.15 0.43 0.3 0.11 0.013)  
##                       948) copd< 0.5 126    64 B2 (0.17 0.49 0.26 0.071 0) *
##                       949) copd>=0.5 111    72 B2 (0.12 0.35 0.35 0.15 0.027)  
##                        1898) age< 75.5 54    30 B3 (0.15 0.26 0.44 0.13 0.019) *
##                        1899) age>=75.5 57    32 B2 (0.088 0.44 0.26 0.18 0.035) *
##                     475) reimbursement2008< 10960 72    44 B3 (0.19 0.22 0.39 0.15 0.042) *
##                 119) reimbursement2008< 8645 540   321 B2 (0.16 0.41 0.32 0.11 0.011)  
##                   238) heart.failure>=0.5 243   128 B2 (0.14 0.47 0.28 0.099 0.016) *
##                   239) heart.failure< 0.5 297   191 B3 (0.18 0.35 0.36 0.11 0.0067)  
##                     478) depression< 0.5 226   141 B2 (0.18 0.38 0.33 0.12 0.0044) *
##                     479) depression>=0.5 71    39 B3 (0.17 0.27 0.45 0.099 0.014) *
##            15) diabetes>=0.5 53801 31450 B2 (0.23 0.42 0.21 0.13 0.02)  
##              30) kidney< 0.5 25067 14311 B2 (0.3 0.43 0.19 0.076 0.0074)  
##                60) arthritis< 0.5 15178  9179 B2 (0.35 0.4 0.17 0.069 0.0063)  
##                 120) cancer< 0.5 12572  7709 B2 (0.39 0.39 0.16 0.063 0.0059)  
##                   240) ihd< 0.5 2617  1376 B1 (0.47 0.34 0.13 0.049 0.0053)  
##                     480) reimbursement2008>=9400 403   171 B1 (0.58 0.21 0.15 0.05 0.0099) *
##                     481) reimbursement2008< 9400 2214  1205 B1 (0.46 0.36 0.13 0.048 0.0045)  
##                       962) osteoporosis< 0.5 1636   847 B1 (0.48 0.34 0.12 0.049 0.0043)  
##                        1924) alzheimers< 0.5 1127   559 B1 (0.5 0.33 0.12 0.042 0.0035) *
##                        1925) alzheimers>=0.5 509   288 B1 (0.43 0.36 0.13 0.065 0.0059)  
##                          3850) reimbursement2008< 3775 137    68 B1 (0.5 0.3 0.12 0.066 0.0073) *
##                          3851) reimbursement2008>=3775 372   220 B1 (0.41 0.39 0.13 0.065 0.0054)  
##                            7702) reimbursement2008>=4055 330   188 B1 (0.43 0.36 0.13 0.07 0.0061)  
##                             15404) reimbursement2008>=4185 309   177 B1 (0.43 0.38 0.12 0.065 0.0065)  
##                               30808) reimbursement2008>=4635 253   138 B1 (0.45 0.35 0.12 0.067 0.0079)  
##                                 61616) age< 96 245   131 B1 (0.47 0.36 0.11 0.065 0.0041)  
##                                  123232) reimbursement2008< 8170 209   106 B1 (0.49 0.33 0.11 0.067 0) *
##                                  123233) reimbursement2008>=8170 36    19 B2 (0.31 0.47 0.14 0.056 0.028)  
##                                    246466) age< 74.5 15     6 B1 (0.6 0.13 0.2 0 0.067) *
##                                    246467) age>=74.5 21     6 B2 (0.095 0.71 0.095 0.095 0) *
##                                 61617) age>=96 8     5 B3 (0.12 0.25 0.38 0.12 0.12) *
##                               30809) reimbursement2008< 4635 56    28 B2 (0.3 0.5 0.14 0.054 0) *
##                             15405) reimbursement2008< 4185 21    11 B1 (0.48 0.095 0.29 0.14 0) *
##                            7703) reimbursement2008< 4055 42    17 B2 (0.24 0.6 0.14 0.024 0) *
##                       963) osteoporosis>=0.5 578   342 B2 (0.38 0.41 0.16 0.047 0.0052)  
##                        1926) depression< 0.5 339   189 B1 (0.44 0.37 0.13 0.047 0.0029)  
##                          3852) reimbursement2008< 4905 211   119 B1 (0.44 0.42 0.11 0.033 0)  
##                            7704) reimbursement2008< 4075 142    71 B1 (0.5 0.36 0.11 0.035 0) *
##                            7705) reimbursement2008>=4075 69    31 B2 (0.3 0.55 0.12 0.029 0) *
##                          3853) reimbursement2008>=4905 128    70 B1 (0.45 0.3 0.17 0.07 0.0078) *
##                        1927) depression>=0.5 239   130 B2 (0.29 0.46 0.2 0.046 0.0084)  
##                          3854) copd< 0.5 181    88 B2 (0.31 0.51 0.13 0.039 0.011) *
##                          3855) copd>=0.5 58    34 B3 (0.24 0.28 0.41 0.069 0) *
##                   241) ihd>=0.5 9955  5976 B2 (0.36 0.4 0.17 0.067 0.006)  
##                     482) depression< 0.5 5563  3339 B1 (0.4 0.38 0.15 0.059 0.0059)  
##                       964) reimbursement2008>=8955 1363   758 B1 (0.44 0.32 0.16 0.067 0.0088)  
##                        1928) copd< 0.5 798   405 B1 (0.49 0.32 0.12 0.064 0.0038) *
##                        1929) copd>=0.5 565   353 B1 (0.38 0.32 0.22 0.073 0.016)  
##                          3858) stroke>=0.5 116    64 B2 (0.35 0.45 0.12 0.06 0.017)  
##                            7716) age>=74.5 63    36 B1 (0.43 0.33 0.16 0.063 0.016) *
##                            7717) age< 74.5 53    22 B2 (0.26 0.58 0.075 0.057 0.019) *
##                          3859) stroke< 0.5 449   278 B1 (0.38 0.28 0.24 0.076 0.016) *
##                       965) reimbursement2008< 8955 4200  2510 B2 (0.39 0.4 0.15 0.056 0.005)  
##                        1930) heart.failure< 0.5 1953  1129 B1 (0.42 0.4 0.13 0.045 0.0041)  
##                          3860) reimbursement2008< 3415 343   172 B1 (0.5 0.37 0.096 0.032 0.0058) *
##                          3861) reimbursement2008>=3415 1610   954 B2 (0.41 0.41 0.14 0.048 0.0037)  
##                            7722) age< 42.5 43    17 B1 (0.6 0.26 0.07 0.07 0) *
##                            7723) age>=42.5 1567   922 B2 (0.4 0.41 0.14 0.047 0.0038)  
##                             15446) age>=50.5 1527   894 B2 (0.4 0.41 0.13 0.047 0.0039)  
##                               30892) reimbursement2008>=3465 1478   870 B2 (0.41 0.41 0.13 0.045 0.0027)  
##                                 61784) reimbursement2008< 4655 759   431 B1 (0.43 0.4 0.12 0.043 0.0013)  
##                                  123568) reimbursement2008>=4315 158    79 B1 (0.5 0.35 0.095 0.051 0.0063) *
##                                  123569) reimbursement2008< 4315 601   352 B1 (0.41 0.41 0.13 0.042 0)  
##                                    247138) reimbursement2008< 4295 592   344 B1 (0.42 0.41 0.13 0.042 0)  
##                                      494276) age>=82.5 135    71 B1 (0.47 0.36 0.15 0.015 0) *
##                                      494277) age< 82.5 457   266 B2 (0.4 0.42 0.13 0.05 0)  
##                                        988554) age< 74.5 290   166 B1 (0.43 0.39 0.13 0.052 0)  
##                                         1977108) age>=62.5 234   128 B1 (0.45 0.38 0.12 0.047 0) *
##                                         1977109) age< 62.5 56    31 B2 (0.32 0.45 0.16 0.071 0) *
##                                        988555) age>=74.5 167    90 B2 (0.36 0.46 0.13 0.048 0)  
##                                         1977110) reimbursement2008>=4105 39    20 B1 (0.49 0.36 0.13 0.026 0) *
##                                         1977111) reimbursement2008< 4105 128    65 B2 (0.32 0.49 0.13 0.055 0) *
##                                    247139) reimbursement2008>=4295 9     1 B2 (0.11 0.89 0 0 0) *
##                                 61785) reimbursement2008>=4655 719   414 B2 (0.38 0.42 0.15 0.047 0.0042)  
##                                  123570) reimbursement2008< 5835 346   180 B2 (0.35 0.48 0.13 0.038 0.0029) *
##                                  123571) reimbursement2008>=5835 373   223 B1 (0.4 0.37 0.16 0.056 0.0054)  
##                                    247142) alzheimers>=0.5 124    64 B1 (0.48 0.31 0.15 0.04 0.0081)  
##                                      494284) reimbursement2008< 8555 114    55 B1 (0.52 0.28 0.16 0.035 0.0088) *
##                                      494285) reimbursement2008>=8555 10     3 B2 (0.1 0.7 0.1 0.1 0) *
##                                    247143) alzheimers< 0.5 249   149 B2 (0.36 0.4 0.17 0.064 0.004)  
##                                      494286) reimbursement2008>=6045 217   124 B2 (0.36 0.43 0.14 0.069 0.0046) *
##                                      494287) reimbursement2008< 6045 32    20 B1 (0.38 0.22 0.38 0.031 0)  
##                                        988574) age< 72.5 11     4 B1 (0.64 0.18 0.18 0 0) *
##                                        988575) age>=72.5 21    11 B3 (0.24 0.24 0.48 0.048 0) *
##                               30893) reimbursement2008< 3465 49    24 B2 (0.27 0.51 0.082 0.1 0.041) *
##                             15447) age< 50.5 40    26 B1 (0.35 0.3 0.3 0.05 0) *
##                        1931) heart.failure>=0.5 2247  1339 B2 (0.35 0.4 0.17 0.066 0.0058)  
##                          3862) reimbursement2008>=5335 866   530 B1 (0.39 0.37 0.16 0.074 0.0058)  
##                            7724) reimbursement2008>=8115 129    68 B2 (0.36 0.47 0.12 0.047 0) *
##                            7725) reimbursement2008< 8115 737   447 B1 (0.39 0.35 0.17 0.079 0.0068)  
##                             15450) age< 94.5 703   421 B1 (0.4 0.35 0.17 0.075 0.0071)  
##                               30900) reimbursement2008>=6635 298   164 B1 (0.45 0.32 0.15 0.067 0.013) *
##                               30901) reimbursement2008< 6635 405   255 B2 (0.37 0.37 0.18 0.081 0.0025)  
##                                 61802) reimbursement2008< 5685 137    80 B1 (0.42 0.31 0.16 0.11 0) *
##                                 61803) reimbursement2008>=5685 268   161 B2 (0.34 0.4 0.19 0.067 0.0037) *
##                             15451) age>=94.5 34    17 B2 (0.24 0.5 0.12 0.15 0) *
##                          3863) reimbursement2008< 5335 1381   795 B2 (0.33 0.42 0.18 0.061 0.0058)  
##                            7726) copd< 0.5 997   591 B2 (0.36 0.41 0.17 0.057 0.006)  
##                             15452) age< 69.5 297   171 B1 (0.42 0.38 0.15 0.04 0.0034)  
##                               30904) reimbursement2008< 5065 274   153 B1 (0.44 0.36 0.15 0.04 0.0036)  
##                                 61808) alzheimers< 0.5 174    91 B1 (0.48 0.3 0.17 0.046 0.0057) *
##                                 61809) alzheimers>=0.5 100    54 B2 (0.38 0.46 0.13 0.03 0)  
##                                  123618) reimbursement2008>=4355 26    10 B1 (0.62 0.15 0.15 0.077 0) *
##                                  123619) reimbursement2008< 4355 74    32 B2 (0.3 0.57 0.12 0.014 0) *
##                               30905) reimbursement2008>=5065 23     9 B2 (0.22 0.61 0.13 0.043 0) *
##                             15453) age>=69.5 700   407 B2 (0.33 0.42 0.18 0.064 0.0071) *
##                            7727) copd>=0.5 384   204 B2 (0.27 0.47 0.19 0.07 0.0052) *
##                     483) depression>=0.5 4392  2538 B2 (0.31 0.42 0.18 0.077 0.0061)  
##                       966) reimbursement2008< 8325 2928  1619 B2 (0.31 0.45 0.17 0.065 0.0065)  
##                        1932) copd< 0.5 1987  1102 B2 (0.33 0.45 0.16 0.056 0.0045)  
##                          3864) age< 98.5 1964  1085 B2 (0.33 0.45 0.16 0.057 0.0046)  
##                            7728) reimbursement2008< 3085 22     8 B1 (0.64 0.23 0.045 0.091 0) *
##                            7729) reimbursement2008>=3085 1942  1068 B2 (0.33 0.45 0.16 0.056 0.0046)  
##                             15458) heart.failure< 0.5 889   508 B2 (0.37 0.43 0.15 0.051 0.0034) *
##                             15459) heart.failure>=0.5 1053   560 B2 (0.3 0.47 0.17 0.061 0.0057)  
##                               30918) osteoporosis< 0.5 721   396 B2 (0.32 0.45 0.16 0.061 0.0055)  
##                                 61836) age>=86.5 109    59 B1 (0.46 0.3 0.15 0.083 0.0092) *
##                                 61837) age< 86.5 612   320 B2 (0.3 0.48 0.16 0.057 0.0049) *
##                               30919) osteoporosis>=0.5 332   164 B2 (0.24 0.51 0.18 0.06 0.006) *
##                          3865) age>=98.5 23    12 B3 (0.26 0.26 0.48 0 0) *
##                        1933) copd>=0.5 941   517 B2 (0.26 0.45 0.2 0.084 0.011) *
##                       967) reimbursement2008>=8325 1464   919 B2 (0.32 0.37 0.2 0.1 0.0055)  
##                        1934) reimbursement2008< 8485 36    16 B1 (0.56 0.22 0.22 0 0) *
##                        1935) reimbursement2008>=8485 1428   891 B2 (0.32 0.38 0.2 0.1 0.0056)  
##                          3870) age< 78.5 837   532 B2 (0.35 0.36 0.19 0.098 0.0036)  
##                            7740) reimbursement2008< 21320 639   406 B1 (0.36 0.35 0.19 0.092 0.0031)  
##                             15480) age< 49.5 83    47 B2 (0.35 0.43 0.096 0.12 0) *
##                             15481) age>=49.5 556   352 B1 (0.37 0.33 0.21 0.088 0.0036)  
##                               30962) age>=67.5 368   230 B1 (0.38 0.36 0.18 0.087 0)  
##                                 61924) reimbursement2008>=10440 261   153 B1 (0.41 0.33 0.17 0.084 0)  
##                                  123848) reimbursement2008< 12585 92    46 B1 (0.5 0.25 0.18 0.065 0) *
##                                  123849) reimbursement2008>=12585 169   105 B2 (0.37 0.38 0.16 0.095 0)  
##                                    247698) reimbursement2008>=14485 109    63 B1 (0.42 0.31 0.17 0.092 0)  
##                                      495396) osteoporosis< 0.5 72    37 B1 (0.49 0.24 0.17 0.11 0) *
##                                      495397) osteoporosis>=0.5 37    20 B2 (0.3 0.46 0.19 0.054 0) *
##                                    247699) reimbursement2008< 14485 60    30 B2 (0.27 0.5 0.13 0.1 0) *
##                                 61925) reimbursement2008< 10440 107    62 B2 (0.28 0.42 0.21 0.093 0) *
##                               30963) age< 67.5 188   122 B1 (0.35 0.28 0.27 0.09 0.011)  
##                                 61926) age>=55.5 135    82 B1 (0.39 0.25 0.27 0.089 0) *
##                                 61927) age< 55.5 53    34 B2 (0.25 0.36 0.26 0.094 0.038) *
##                            7741) reimbursement2008>=21320 198   114 B2 (0.3 0.42 0.16 0.12 0.0051) *
##                          3871) age>=78.5 591   359 B2 (0.28 0.39 0.21 0.11 0.0085)  
##                            7742) heart.failure< 0.5 122    73 B1 (0.4 0.32 0.18 0.098 0)  
##                             15484) reimbursement2008< 11560 40    17 B1 (0.58 0.2 0.18 0.05 0) *
##                             15485) reimbursement2008>=11560 82    51 B2 (0.32 0.38 0.18 0.12 0) *
##                            7743) heart.failure>=0.5 469   276 B2 (0.24 0.41 0.22 0.11 0.011) *
##                 121) cancer>=0.5 2606  1470 B2 (0.21 0.44 0.25 0.098 0.0081) *
##                61) arthritis>=0.5 9889  5132 B2 (0.22 0.48 0.21 0.088 0.0092)  
##                 122) depression< 0.5 5134  2665 B2 (0.25 0.48 0.18 0.08 0.0078)  
##                   244) cancer< 0.5 4305  2260 B2 (0.27 0.48 0.17 0.076 0.0086)  
##                     488) reimbursement2008>=9880 1063   636 B2 (0.32 0.4 0.18 0.089 0.012)  
##                       976) ihd< 0.5 102    49 B1 (0.52 0.27 0.13 0.069 0.0098) *
##                       977) ihd>=0.5 961   562 B2 (0.29 0.42 0.19 0.092 0.012) *
##                     489) reimbursement2008< 9880 3242  1624 B2 (0.25 0.5 0.17 0.072 0.0074) *
##                   245) cancer>=0.5 829   405 B2 (0.15 0.51 0.23 0.1 0.0036) *
##                 123) depression>=0.5 4755  2467 B2 (0.18 0.48 0.23 0.096 0.011) *
##              31) kidney>=0.5 28734 17139 B2 (0.16 0.4 0.23 0.17 0.03)  
##                62) reimbursement2008< 15395 16249  9131 B2 (0.19 0.44 0.24 0.12 0.016)  
##                 124) arthritis< 0.5 9424  5647 B2 (0.23 0.4 0.23 0.12 0.017)  
##                   248) cancer< 0.5 7786  4711 B2 (0.25 0.39 0.21 0.12 0.017)  
##                     496) ihd< 0.5 964   608 B1 (0.37 0.36 0.17 0.085 0.011)  
##                       992) depression< 0.5 572   338 B1 (0.41 0.32 0.16 0.1 0.01)  
##                        1984) reimbursement2008< 3545 101    53 B2 (0.33 0.48 0.15 0.04 0.0099) *
##                        1985) reimbursement2008>=3545 471   270 B1 (0.43 0.29 0.16 0.11 0.011)  
##                          3970) osteoporosis< 0.5 346   186 B1 (0.46 0.27 0.15 0.11 0.014) *
##                          3971) osteoporosis>=0.5 125    82 B2 (0.33 0.34 0.2 0.13 0)  
##                            7942) age>=62 106    67 B1 (0.37 0.37 0.15 0.11 0)  
##                             15884) age>=67.5 93    55 B2 (0.34 0.41 0.16 0.086 0)  
##                               31768) reimbursement2008>=6110 44    23 B1 (0.48 0.3 0.11 0.11 0)  
##                                 63536) reimbursement2008< 9180 26     9 B1 (0.65 0.15 0.077 0.12 0) *
##                                 63537) reimbursement2008>=9180 18     9 B2 (0.22 0.5 0.17 0.11 0) *
##                               31769) reimbursement2008< 6110 49    24 B2 (0.22 0.51 0.2 0.061 0) *
##                             15885) age< 67.5 13     6 B1 (0.54 0.077 0.077 0.31 0) *
##                            7943) age< 62 19    10 B3 (0.11 0.21 0.47 0.21 0) *
##                       993) depression>=0.5 392   227 B2 (0.31 0.42 0.19 0.064 0.013)  
##                        1986) reimbursement2008>=14460 9     2 B1 (0.78 0.22 0 0 0) *
##                        1987) reimbursement2008< 14460 383   220 B2 (0.3 0.43 0.2 0.065 0.013) *
##                     497) ihd>=0.5 6822  4095 B2 (0.24 0.4 0.22 0.12 0.018)  
##                       994) reimbursement2008< 6325 3172  1786 B2 (0.22 0.44 0.22 0.11 0.016) *
##                       995) reimbursement2008>=6325 3650  2309 B2 (0.25 0.37 0.22 0.14 0.019)  
##                        1990) osteoporosis< 0.5 2424  1594 B2 (0.27 0.34 0.23 0.14 0.02)  
##                          3980) depression< 0.5 1234   816 B2 (0.3 0.34 0.2 0.13 0.024)  
##                            7960) reimbursement2008>=12135 349   226 B1 (0.35 0.3 0.16 0.16 0.032)  
##                             15920) age>=54 331   210 B1 (0.37 0.28 0.16 0.16 0.03) *
##                             15921) age< 54 18     9 B2 (0.11 0.5 0.11 0.22 0.056) *
##                            7961) reimbursement2008< 12135 885   570 B2 (0.28 0.36 0.22 0.12 0.021) *
##                          3981) depression>=0.5 1190   778 B2 (0.24 0.35 0.25 0.15 0.016)  
##                            7962) copd< 0.5 547   367 B2 (0.28 0.33 0.25 0.12 0.022)  
##                             15924) reimbursement2008>=9205 310   209 B1 (0.33 0.32 0.21 0.12 0.029)  
##                               31848) reimbursement2008< 9955 50    28 B2 (0.42 0.44 0.02 0.12 0) *
##                               31849) reimbursement2008>=9955 260   180 B1 (0.31 0.3 0.24 0.12 0.035)  
##                                 63698) reimbursement2008>=14765 20     9 B1 (0.55 0.25 0.1 0.05 0.05) *
##                                 63699) reimbursement2008< 14765 240   168 B2 (0.29 0.3 0.25 0.12 0.033)  
##                                  127398) age>=61.5 201   138 B1 (0.31 0.29 0.24 0.13 0.02)  
##                                    254796) reimbursement2008< 12625 112    69 B1 (0.38 0.24 0.28 0.089 0.0089) *
##                                    254797) reimbursement2008>=12625 89    58 B2 (0.22 0.35 0.2 0.19 0.034) *
##                                  127399) age< 61.5 39    25 B2 (0.15 0.36 0.31 0.077 0.1) *
##                             15925) reimbursement2008< 9205 237   156 B2 (0.22 0.34 0.3 0.12 0.013)  
##                               31850) age< 67.5 56    29 B2 (0.2 0.48 0.16 0.16 0) *
##                               31851) age>=67.5 181   118 B3 (0.23 0.3 0.35 0.1 0.017)  
##                                 63702) reimbursement2008>=6865 136    82 B3 (0.25 0.26 0.4 0.074 0.015) *
##                                 63703) reimbursement2008< 6865 45    27 B2 (0.18 0.4 0.2 0.2 0.022) *
##                            7963) copd>=0.5 643   411 B2 (0.21 0.36 0.25 0.17 0.011) *
##                        1991) osteoporosis>=0.5 1226   715 B2 (0.21 0.42 0.22 0.14 0.017) *
##                   249) cancer>=0.5 1638   936 B2 (0.13 0.43 0.29 0.14 0.016) *
##                 125) arthritis>=0.5 6825  3484 B2 (0.13 0.49 0.25 0.12 0.014) *
##                63) reimbursement2008>=15395 12485  8008 B2 (0.13 0.36 0.23 0.24 0.049)  
##                 126) arthritis>=0.5 5402  3220 B2 (0.094 0.4 0.24 0.22 0.04)  
##                   252) reimbursement2008< 34925 3345  1942 B2 (0.11 0.42 0.25 0.19 0.03)  
##                     504) depression< 0.5 1291   714 B2 (0.14 0.45 0.22 0.17 0.025)  
##                      1008) cancer< 0.5 973   546 B2 (0.16 0.44 0.19 0.18 0.029) *
##                      1009) cancer>=0.5 318   168 B2 (0.072 0.47 0.3 0.14 0.013)  
##                        2018) reimbursement2008>=16525 293   144 B2 (0.068 0.51 0.28 0.13 0.014) *
##                        2019) reimbursement2008< 16525 25    10 B3 (0.12 0.04 0.6 0.24 0) *
##                     505) depression>=0.5 2054  1228 B2 (0.092 0.4 0.27 0.2 0.034) *
##                   253) reimbursement2008>=34925 2057  1278 B2 (0.067 0.38 0.23 0.27 0.055)  
##                     506) copd< 0.5 520   300 B2 (0.096 0.42 0.23 0.21 0.042) *
##                     507) copd>=0.5 1537   978 B2 (0.057 0.36 0.23 0.29 0.06)  
##                      1014) age>=62.5 1286   804 B2 (0.058 0.37 0.24 0.27 0.061) *
##                      1015) age< 62.5 251   153 B4 (0.052 0.31 0.2 0.39 0.052)  
##                        2030) reimbursement2008< 101585 237   150 B4 (0.055 0.32 0.21 0.37 0.051)  
##                          4060) cancer>=0.5 62    36 B2 (0.048 0.42 0.27 0.24 0.016) *
##                          4061) cancer< 0.5 175   103 B4 (0.057 0.29 0.18 0.41 0.063) *
##                        2031) reimbursement2008>=101585 14     3 B4 (0 0.071 0.071 0.79 0.071) *
##                 127) arthritis< 0.5 7083  4788 B2 (0.15 0.32 0.22 0.25 0.057)  
##                   254) cancer< 0.5 5298  3651 B2 (0.17 0.31 0.2 0.26 0.062)  
##                     508) depression< 0.5 2489  1797 B2 (0.22 0.28 0.18 0.27 0.06)  
##                      1016) copd>=0.5 1317   890 B2 (0.2 0.32 0.18 0.24 0.056)  
##                        2032) ihd< 0.5 72    41 B1 (0.43 0.25 0.15 0.11 0.056) *
##                        2033) ihd>=0.5 1245   836 B2 (0.19 0.33 0.18 0.24 0.056) *
##                      1017) copd< 0.5 1172   815 B4 (0.23 0.23 0.17 0.3 0.065)  
##                        2034) reimbursement2008>=43640 191   129 B2 (0.15 0.32 0.23 0.22 0.073)  
##                          4068) age>=64.5 172   112 B2 (0.16 0.35 0.2 0.23 0.064) *
##                          4069) age< 64.5 19    10 B3 (0.11 0.11 0.47 0.16 0.16) *
##                        2035) reimbursement2008< 43640 981   666 B4 (0.25 0.21 0.16 0.32 0.063)  
##                          4070) reimbursement2008< 23175 468   337 B1 (0.28 0.24 0.16 0.26 0.056)  
##                            8140) age< 93.5 457   326 B1 (0.29 0.23 0.16 0.26 0.057)  
##                             16280) age< 86.5 398   288 B1 (0.28 0.25 0.16 0.25 0.063)  
##                               32560) alzheimers>=0.5 179   122 B1 (0.32 0.28 0.15 0.17 0.073)  
##                                 65120) reimbursement2008>=21440 38    19 B2 (0.24 0.5 0.11 0.13 0.026) *
##                                 65121) reimbursement2008< 21440 141    93 B1 (0.34 0.23 0.16 0.18 0.085)  
##                                  130242) reimbursement2008>=17585 89    53 B1 (0.4 0.17 0.19 0.16 0.079) *
##                                  130243) reimbursement2008< 17585 52    35 B2 (0.23 0.33 0.12 0.23 0.096) *
##                               32561) alzheimers< 0.5 219   151 B4 (0.24 0.23 0.16 0.31 0.055) *
##                             16281) age>=86.5 59    38 B1 (0.36 0.1 0.17 0.36 0.017)  
##                               32562) reimbursement2008>=19680 18     8 B1 (0.56 0.11 0 0.28 0.056) *
##                               32563) reimbursement2008< 19680 41    25 B4 (0.27 0.098 0.24 0.39 0) *
##                            8141) age>=93.5 11     6 B2 (0 0.45 0.36 0.18 0) *
##                          4071) reimbursement2008>=23175 513   320 B4 (0.22 0.18 0.16 0.38 0.07) *
##                     509) depression>=0.5 2809  1854 B2 (0.13 0.34 0.22 0.25 0.063) *
##                   255) cancer>=0.5 1785  1137 B2 (0.097 0.36 0.28 0.22 0.041) *
## [1] TRUE
replay.petrisim(pn=glb_analytics_pn, 
    replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "model.selected")), flip_coord=TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0 
## 2.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction   firing:  model.selected 
## 3.0000    3   0 2 1 0

glb_script_df <- rbind(glb_script_df, 
                   data.frame(chunk_label="fit.data.training.all", 
                              chunk_step_major=max(glb_script_df$chunk_step_major)+1, 
                              chunk_step_minor=0,
                              elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))
##                    chunk_label chunk_step_major chunk_step_minor  elapsed
## elapsed8            fit.models                5                0   71.848
## elapsed9 fit.data.training.all                6                0 9768.275

Step 6: fit.data.training.all

if (glb_fin_mdl_id %in% names(glb_models_lst)) {
    warning("Final model same as user selected model")
    glb_fin_mdl <- glb_sel_mdl
} else {    
    print(mdl_feats_df <- myextract_mdl_feats( sel_mdl=glb_sel_mdl, 
                                               entity_df=glb_entity_df))
    
    # Sync with parameters in mydsutils.R
    ret_lst <- myfit_mdl_fn(model_id="Final",
                            indep_vars_vctr=mdl_feats_df$id,
                            rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out, 
                            fit_df=glb_entity_df,
                            model_method=glb_sel_mdl$method,
                            model_loss_mtrx=glb_model_metric_terms, # Automate this
                            model_summaryFunction=glb_sel_mdl$control$summaryFunction,
                            model_metric=glb_sel_mdl$metric,
                            model_metric_maximize=glb_sel_mdl$maximize)
    glb_fin_mdl <- glb_models_lst[["Final"]] 
}
## Warning: Final model same as user selected model
glb_script_df <- rbind(glb_script_df, 
                   data.frame(chunk_label="fit.data.training.all", 
    chunk_step_major=glb_script_df[nrow(glb_script_df), "chunk_step_major"], 
    chunk_step_minor=glb_script_df[nrow(glb_script_df), "chunk_step_minor"]+1,
                              elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))
##                     chunk_label chunk_step_major chunk_step_minor  elapsed
## elapsed9  fit.data.training.all                6                0 9768.275
## elapsed10 fit.data.training.all                6                1 9789.672
if (glb_is_regression) {
    glb_entity_df[, glb_rsp_var_out] <- predict(glb_fin_mdl, newdata=glb_entity_df)
    print(myplot_scatter(glb_entity_df, glb_rsp_var, glb_rsp_var_out, 
                         smooth=TRUE))
    glb_entity_df[, paste0(glb_rsp_var_out, ".err")] <- 
        abs(glb_entity_df[, glb_rsp_var_out] - glb_entity_df[, glb_rsp_var])
    print(head(orderBy(reformulate(c("-", paste0(glb_rsp_var_out, ".err"))), 
                       glb_entity_df)))                             
}    

if (glb_is_classification & glb_is_binomial) {
    stop("not implemented")
            if (any(class(glb_fin_mdl) %in% c("train"))) {
        glb_entity_df[, paste0(glb_rsp_var_out, ".proba")] <- 
            predict(glb_fin_mdl, newdata=glb_entity_df, type="prob")[, 2]
    } else  if (any(class(glb_fin_mdl) %in% c("rpart", "randomForest"))) {
        glb_entity_df[, paste0(glb_rsp_var_out, ".proba")] <- 
            predict(glb_fin_mdl, newdata=glb_entity_df, type="prob")[, 2]
    } else  if (class(glb_fin_mdl) == "glm") {
        stop("not implemented yet")
        glb_entity_df[, paste0(glb_rsp_var_out, ".proba")] <- 
            predict(glb_fin_mdl, newdata=glb_entity_df, type="response")
    } else  stop("not implemented yet")   

    require(ROCR)
    ROCRpred <- prediction(glb_entity_df[, paste0(glb_rsp_var_out, ".proba")],
                           glb_entity_df[, glb_rsp_var])
    ROCRperf <- performance(ROCRpred, "tpr", "fpr")
    plot(ROCRperf, colorize=TRUE, print.cutoffs.at=seq(0, 1, 0.1), text.adj=c(-0.2,1.7))
    
    thresholds_df <- data.frame(threshold=seq(0.0, 1.0, 0.1))
    thresholds_df$f.score <- sapply(1:nrow(thresholds_df), function(row_ix) 
        mycompute_classifier_f.score(mdl=glb_fin_mdl, obs_df=glb_entity_df, 
                                     proba_threshold=thresholds_df[row_ix, "threshold"], 
                                      rsp_var=glb_rsp_var, 
                                      rsp_var_out=glb_rsp_var_out))
    print(thresholds_df)
    print(myplot_line(thresholds_df, "threshold", "f.score"))
    
    proba_threshold <- thresholds_df[which.max(thresholds_df$f.score), 
                                             "threshold"]
    # This should change to maximize f.score.OOB ???
    print(sprintf("Classifier Probability Threshold: %0.4f to maximize f.score.fit",
                  proba_threshold))
    if (is.null(glb_clf_proba_threshold)) 
        glb_clf_proba_threshold <- proba_threshold else {
        print(sprintf("Classifier Probability Threshold: %0.4f per user specs",
                      glb_clf_proba_threshold))
    }

    if ((class(glb_entity_df[, glb_rsp_var]) != "factor") | 
        (length(levels(glb_entity_df[, glb_rsp_var])) != 2))
        stop("expecting a factor with two levels:", glb_rsp_var)
    glb_entity_df[, glb_rsp_var_out] <- 
        factor(levels(glb_entity_df[, glb_rsp_var])[
            (glb_entity_df[, paste0(glb_rsp_var_out, ".proba")] >= 
                glb_clf_proba_threshold) * 1 + 1])
             
    print(mycreate_xtab(glb_entity_df, c(glb_rsp_var, glb_rsp_var_out)))
    print(sprintf("f.score=%0.4f", 
        mycompute_classifier_f.score(glb_fin_mdl, glb_entity_df, 
                                     glb_clf_proba_threshold, 
                                     glb_rsp_var, glb_rsp_var_out)))    
}

if (glb_is_classification & !glb_is_binomial) {
    glb_entity_df[, glb_rsp_var_out] <- predict(glb_fin_mdl, newdata=glb_entity_df, type="raw")
}    

print(glb_feats_df <- mymerge_feats_importance(feats_df=glb_feats_df, sel_mdl=glb_fin_mdl, 
                                               entity_df=glb_entity_df))
##                   id         cor.y exclude.as.feat    cor.y.abs cor.low
## 16 reimbursement2008  0.3756473063               0 0.3756473063       0
## 5         bucket2008  0.4518935763               0 0.4518935763       1
## 11          diabetes  0.3904719536               0 0.3904719536       1
## 13               ihd  0.3905905884               0 0.3905905884       1
## 12     heart.failure  0.3647689526               0 0.3647689526       1
## 14            kidney  0.3683780944               0 0.3683780944       1
## 4          arthritis  0.2717113526               0 0.2717113526       1
## 10        depression  0.2835366153               0 0.2835366153       1
## 8             cancer  0.2100892954               0 0.2100892954       1
## 2                age  0.0495694151               0 0.0495694151       1
## 9               copd  0.3108325355               0 0.3108325355       1
## 15      osteoporosis  0.2076745377               0 0.2076745377       1
## 3         alzheimers  0.2741643394               0 0.2741643394       1
## 18            stroke  0.1846626746               0 0.1846626746       1
## 1             .rnorm -0.0007970401               0 0.0007970401       1
## 6    bucket2008.fctr  0.4518935763               1 0.4518935763       0
## 7         bucket2009  1.0000000000               1 1.0000000000       0
## 17 reimbursement2009  0.8581631864               1 0.8581631864       0
##      importance
## 16 100.00000000
## 5   83.20987115
## 11  64.93876310
## 13  62.97689485
## 12  52.37678436
## 14   9.66286974
## 4    5.84119903
## 10   2.26938048
## 8    1.78931779
## 2    1.02716655
## 9    0.89198875
## 15   0.66926118
## 3    0.09536685
## 18   0.00000000
## 1            NA
## 6            NA
## 7            NA
## 17           NA
# Most of this code is used again in predict.data.new chunk
glb_analytics_diag_plots <- function(obs_df) {
    for (var in subset(glb_feats_df, !is.na(importance))$id) {
        plot_df <- melt(obs_df, id.vars=var, 
                        measure.vars=c(glb_rsp_var, glb_rsp_var_out))
#         if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value", 
#                                              facet_colcol_name="variable") + 
#                       geom_vline(xintercept=<divider_val>, linetype="dotted")) else     
            print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
                                 facet_colcol_name="variable", jitter=TRUE) + 
                      guides(color=FALSE))
    }
    
    if (glb_is_regression) {
        plot_vars_df <- subset(glb_feats_df, Pr.z < 0.1)
        print(myplot_prediction_regression(obs_df, 
                    ifelse(nrow(plot_vars_df) > 1, plot_vars_df$id[2], ".rownames"), 
                                           plot_vars_df$id[1],
                    glb_rsp_var, glb_rsp_var_out)
#               + facet_wrap(reformulate(plot_vars_df$id[2])) # if [1,2] is a factor                                                         
#               + geom_point(aes_string(color="<col_name>.fctr")) #  to color the plot
              )
    }    
    
    if (glb_is_classification) {
        if (nrow(plot_vars_df <- subset(glb_feats_df, !is.na(importance))) == 0)
            warning("No features in selected model are statistically important")
        else print(myplot_prediction_classification(df=obs_df, 
                feat_x=ifelse(nrow(plot_vars_df) > 1, plot_vars_df$id[2], 
                              ".rownames"),
                                               feat_y=plot_vars_df$id[1],
                     rsp_var=glb_rsp_var, 
                     rsp_var_out=glb_rsp_var_out, 
                     id_vars=glb_id_vars)
#               + geom_hline(yintercept=<divider_val>, linetype = "dotted")
                )
    }    
}
glb_analytics_diag_plots(obs_df=glb_entity_df)

##       age alzheimers arthritis cancer copd depression diabetes
## 1      85          0         0      0    0          0        0
## 90001  78          0         0      1    0          1        1
## 90003  83          1         0      0    0          1        1
## 90005  91          0         0      0    0          1        1
## 90010  84          0         0      0    0          0        1
## 90491  73          0         0      0    1          0        0
##       heart.failure ihd kidney osteoporosis stroke reimbursement2008
## 1                 0   0      0            0      0                 0
## 90001             1   1      1            1      0              2720
## 90003             1   1      1            0      0              2740
## 90005             0   1      0            1      0              2810
## 90010             0   1      0            1      0              2970
## 90491             1   0      1            0      0             55000
##       bucket2008 reimbursement2009 bucket2009      .rnorm bucket2009.fctr
## 1              1                 0          1 -0.92480010              B1
## 90001          1                 0          1  0.46373167              B1
## 90003          1                 0          1 -0.19796903              B1
## 90005          1                 0          1  0.92002065              B1
## 90010          1                 0          1  0.04393011              B1
## 90491          5                 0          1  1.17487693              B1
##       bucket2008.fctr bucket2009.fctr.predict.
## 1                  B1                       B1
## 90001              B1                       B2
## 90003              B1                       B2
## 90005              B1                       B2
## 90010              B1                       B2
## 90491              B5                       B1
##       bucket2009.fctr.predict..accurate .label
## 1                                  TRUE     .1
## 90001                             FALSE .90001
## 90003                             FALSE .90003
## 90005                             FALSE .90005
## 90010                             FALSE .90010
## 90491                              TRUE .90491
##        age alzheimers arthritis cancer copd depression diabetes
## 90003   83          1         0      0    0          1        1
## 105382  62          0         0      1    1          1        0
## 127997  62          1         0      1    1          1        1
## 307449  66          0         0      0    0          0        0
## 307451  86          0         0      0    0          0        0
## 366459  71          1         0      0    1          1        1
##        heart.failure ihd kidney osteoporosis stroke reimbursement2008
## 90003              1   1      1            0      0              2740
## 105382             1   1      1            0      0             65580
## 127997             1   1      0            0      1             60020
## 307449             0   0      0            0      0                 0
## 307451             0   0      0            0      0                 0
## 366459             1   1      1            0      0            194910
##        bucket2008 reimbursement2009 bucket2009      .rnorm bucket2009.fctr
## 90003           1                 0          1 -0.19796903              B1
## 105382          5                70          1  1.40881604              B1
## 127997          5               240          1  0.25106429              B1
## 307449          1              3000          2  0.03560744              B2
## 307451          1              3000          2 -1.58625862              B2
## 366459          5              5400          2  1.64137090              B2
##        bucket2008.fctr bucket2009.fctr.predict.
## 90003               B1                       B2
## 105382              B5                       B2
## 127997              B5                       B2
## 307449              B1                       B1
## 307451              B1                       B1
## 366459              B5                       B2
##        bucket2009.fctr.predict..accurate  .label
## 90003                              FALSE  .90003
## 105382                             FALSE .105382
## 127997                             FALSE .127997
## 307449                             FALSE .307449
## 307451                             FALSE .307451
## 366459                              TRUE .366459
##        age alzheimers arthritis cancer copd depression diabetes
## 307446  66          0         0      0    0          0        0
## 307449  66          0         0      0    0          0        0
## 307450  76          0         0      0    0          0        0
## 307451  86          0         0      0    0          0        0
## 307452  68          0         0      0    0          0        0
## 366459  71          1         0      0    1          1        1
##        heart.failure ihd kidney osteoporosis stroke reimbursement2008
## 307446             0   0      0            0      0                 0
## 307449             0   0      0            0      0                 0
## 307450             0   0      0            0      0                 0
## 307451             0   0      0            0      0                 0
## 307452             0   0      0            0      0                 0
## 366459             1   1      1            0      0            194910
##        bucket2008 reimbursement2009 bucket2009      .rnorm bucket2009.fctr
## 307446          1              3000          2  0.32846944              B2
## 307449          1              3000          2  0.03560744              B2
## 307450          1              3000          2 -2.09348196              B2
## 307451          1              3000          2 -1.58625862              B2
## 307452          1              3000          2 -0.22660343              B2
## 366459          5              5400          2  1.64137090              B2
##        bucket2008.fctr bucket2009.fctr.predict.
## 307446              B1                       B1
## 307449              B1                       B1
## 307450              B1                       B1
## 307451              B1                       B1
## 307452              B1                       B1
## 366459              B5                       B2
##        bucket2009.fctr.predict..accurate  .label
## 307446                             FALSE .307446
## 307449                             FALSE .307449
## 307450                             FALSE .307450
## 307451                             FALSE .307451
## 307452                             FALSE .307452
## 366459                              TRUE .366459

replay.petrisim(pn=glb_analytics_pn, 
    replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "data.training.all.prediction","model.final")), flip_coord=TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0 
## 2.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction   firing:  model.selected 
## 3.0000    3   0 2 1 0 
## 3.0000   multiple enabled transitions:  model.final data.training.all.prediction data.new.prediction     firing:  data.training.all.prediction 
## 4.0000    5   0 1 1 1 
## 4.0000   multiple enabled transitions:  model.final data.training.all.prediction data.new.prediction     firing:  model.final 
## 5.0000    4   0 0 2 1

glb_script_df <- rbind(glb_script_df, 
                   data.frame(chunk_label="predict.data.new", 
                              chunk_step_major=max(glb_script_df$chunk_step_major)+1, 
                              chunk_step_minor=0,
                              elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))
##                     chunk_label chunk_step_major chunk_step_minor
## elapsed10 fit.data.training.all                6                1
## elapsed11      predict.data.new                7                0
##             elapsed
## elapsed10  9789.672
## elapsed11 10395.511

Step 7: predict data.new

if (glb_is_regression)
    glb_newent_df[, glb_rsp_var_out] <- predict(glb_fin_mdl, 
                                        newdata=glb_newent_df, type="response")

if (glb_is_classification & glb_is_binomial) {
    # Compute selected model predictions
            if (any(class(glb_fin_mdl) %in% c("train"))) {
        glb_newent_df[, paste0(glb_rsp_var_out, ".proba")] <- 
            predict(glb_fin_mdl, newdata=glb_newent_df, type="prob")[, 2]
    } else  if (any(class(glb_fin_mdl) %in% c("rpart", "randomForest"))) {
        glb_newent_df[, paste0(glb_rsp_var_out, ".proba")] <- 
            predict(glb_fin_mdl, newdata=glb_newent_df, type="prob")[, 2]
    } else  if (class(glb_fin_mdl) == "glm") {
        stop("not implemented yet")
        glb_newent_df[, paste0(glb_rsp_var_out, ".proba")] <- 
            predict(glb_fin_mdl, newdata=glb_newent_df, type="response")
    } else  stop("not implemented yet")   

    if ((class(glb_newent_df[, glb_rsp_var]) != "factor") | 
        (length(levels(glb_newent_df[, glb_rsp_var])) != 2))
        stop("expecting a factor with two levels:", glb_rsp_var)
    glb_newent_df[, glb_rsp_var_out] <- 
        factor(levels(glb_newent_df[, glb_rsp_var])[
            (glb_newent_df[, paste0(glb_rsp_var_out, ".proba")] >= 
                glb_clf_proba_threshold) * 1 + 1])

    # Compute dummy model predictions
    glb_newent_df[, paste0(glb_rsp_var, ".predictdmy.proba")] <- 
        predict(glb_dmy_mdl, newdata=glb_newent_df, type="prob")[, 2]
    if ((class(glb_newent_df[, glb_rsp_var]) != "factor") | 
        (length(levels(glb_newent_df[, glb_rsp_var])) != 2))
        stop("expecting a factor with two levels:", glb_rsp_var)
    glb_newent_df[, paste0(glb_rsp_var, ".predictdmy")] <- 
        factor(levels(glb_newent_df[, glb_rsp_var])[
            (glb_newent_df[, paste0(glb_rsp_var, ".predictdmy.proba")] >= 
                glb_clf_proba_threshold) * 1 + 1])
}

if (glb_is_classification & !glb_is_binomial) {    
    # Compute final model predictions
    glb_rsp_var_out <- paste0(glb_rsp_var_out, "Final")
    glb_newent_df[, glb_rsp_var_out] <- 
        mypredict_mdl(glb_fin_mdl, glb_newent_df, glb_rsp_var, glb_rsp_var_out, 
                      "Final", "Final",
                                glb_model_metric_smmry, glb_model_metric, 
                                glb_model_metric_maximize, ret_type="raw")
}
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 114141   8610    124    103      0
##        B2  18409  16102    187    142      0
##        B3   8027   8146    118     99      0
##        B4   3099   4584     53    201      0
##        B5    351    657      4     45      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.126669e-01             NA   7.105887e-01   7.147384e-01   6.712700e-01 
## AccuracyPValue  McnemarPValue 
##  7.015238e-319   0.000000e+00
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
myprint_df(glb_newent_df[, c(glb_id_vars, glb_rsp_var, glb_rsp_var_out)])
##    bucket2009.fctr bucket2009.fctr.predict.Final
## 3               B1                            B1
## 5               B1                            B1
## 6               B1                            B1
## 8               B1                            B1
## 9               B1                            B1
## 10              B1                            B1
##        bucket2009.fctr bucket2009.fctr.predict.Final
## 41663               B1                            B1
## 152431              B1                            B1
## 189586              B1                            B1
## 254336              B1                            B2
## 310981              B2                            B1
## 350192              B2                            B1
##        bucket2009.fctr bucket2009.fctr.predict.Final
## 457996              B5                            B2
## 457998              B5                            B2
## 458001              B5                            B2
## 458002              B5                            B2
## 458003              B5                            B2
## 458005              B5                            B2
if (glb_is_regression) {
    print(sprintf("Total SSE: %0.4f", 
                  sum((glb_newent_df[, glb_rsp_var_out] - 
                        glb_newent_df[, glb_rsp_var]) ^ 2)))
    print(sprintf("RMSE: %0.4f", 
                  (sum((glb_newent_df[, glb_rsp_var_out] - 
                        glb_newent_df[, glb_rsp_var]) ^ 2) / nrow(glb_newent_df)) ^ 0.5))                        
    print(myplot_scatter(glb_newent_df, glb_rsp_var, glb_rsp_var_out, 
                         smooth=TRUE))
                         
    glb_newent_df[, paste0(glb_rsp_var_out, ".err")] <- 
        abs(glb_newent_df[, glb_rsp_var_out] - glb_newent_df[, glb_rsp_var])
    print(head(orderBy(reformulate(c("-", paste0(glb_rsp_var_out, ".err"))), 
                       glb_newent_df)))                                                      

#     glb_newent_df[, "<Output Pred variable>"] <- func(glb_newent_df[, glb_pred_var_name])                         
}                         

if (glb_is_classification & glb_is_binomial) {
    ROCRpred <- prediction(glb_newent_df[, paste0(glb_rsp_var_out, ".proba")],
                           glb_newent_df[, glb_rsp_var])
    print(sprintf("auc=%0.4f", auc <- as.numeric(performance(ROCRpred, "auc")@y.values)))   
    
    print(sprintf("probability threshold=%0.4f", glb_clf_proba_threshold))
    print(newent_conf_df <- mycreate_xtab(glb_newent_df, 
                                        c(glb_rsp_var, glb_rsp_var_out)))
    print(sprintf("f.score.sel=%0.4f", 
        mycompute_classifier_f.score(mdl=glb_fin_mdl, obs_df=glb_newent_df, 
                                     proba_threshold=glb_clf_proba_threshold, 
                                      rsp_var=glb_rsp_var, 
                                      rsp_var_out=glb_rsp_var_out)))
    print(sprintf("sensitivity=%0.4f", newent_conf_df[2, 3] / 
                      (newent_conf_df[2, 3] + newent_conf_df[2, 2])))
    print(sprintf("specificity=%0.4f", newent_conf_df[1, 2] / 
                      (newent_conf_df[1, 2] + newent_conf_df[1, 3])))
    print(sprintf("accuracy=%0.4f", (newent_conf_df[1, 2] + newent_conf_df[2, 3]) / 
                      (newent_conf_df[1, 2] + newent_conf_df[2, 3] + 
                       newent_conf_df[1, 3] + newent_conf_df[2, 2])))
    
    print(mycreate_xtab(glb_newent_df, c(glb_rsp_var, paste0(glb_rsp_var, ".predictdmy"))))
    print(sprintf("f.score.dmy=%0.4f", 
        mycompute_classifier_f.score(mdl=glb_dmy_mdl, obs_df=glb_newent_df, 
                                     proba_threshold=glb_clf_proba_threshold, 
                                      rsp_var=glb_rsp_var, 
                                      rsp_var_out=paste0(glb_rsp_var, ".predictdmy"))))
}    

if (glb_is_classification & !glb_is_binomial) {
    print(mypredict_mdl(glb_fin_mdl, glb_newent_df, glb_rsp_var, glb_rsp_var_out, 
                      "Final", "Final",
                                glb_model_metric_smmry, glb_model_metric, 
                                glb_model_metric_maximize, ret_type="stats"))    
}    
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##          Prediction
## Reference     B1     B2     B3     B4     B5
##        B1 114141   8610    124    103      0
##        B2  18409  16102    187    142      0
##        B3   8027   8146    118     99      0
##        B4   3099   4584     53    201      0
##        B5    351    657      4     45      0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.126669e-01             NA   7.105887e-01   7.147384e-01   6.712700e-01 
## AccuracyPValue  McnemarPValue 
##  7.015238e-319   0.000000e+00
## Warning in ni[1:m] * nj[1:m]: NAs produced by integer overflow
##   model_id max.Accuracy.Final max.AccuracyLower.Final
## 1    Final          0.7126669               0.7105887
##   max.AccuracyUpper.Final max.Kappa.Final min.loss.error.Final
## 1               0.7147384              NA            0.7578902
glb_analytics_diag_plots(obs_df=glb_newent_df)

##       age alzheimers arthritis cancer copd depression diabetes
## 3      67          0         0      0    0          0        0
## 89984  72          0         1      0    0          0        1
## 89990  86          0         0      0    0          1        1
## 90000  80          0         0      0    0          0        1
## 90002  76          0         1      0    0          0        1
## 90008  79          0         0      0    0          1        1
##       heart.failure ihd kidney osteoporosis stroke reimbursement2008
## 3                 0   0      0            0      0                 0
## 89984             1   1      1            0      0              2420
## 89990             1   1      1            0      0              2530
## 90000             1   1      1            0      0              2720
## 90002             1   1      1            0      0              2730
## 90008             1   1      0            0      1              2900
##       bucket2008 reimbursement2009 bucket2009     .rnorm bucket2009.fctr
## 3              1                 0          1  0.7183313              B1
## 89984          1                 0          1  1.9883004              B1
## 89990          1                 0          1 -0.5157387              B1
## 90000          1                 0          1  0.8442956              B1
## 90002          1                 0          1 -0.8136275              B1
## 90008          1                 0          1 -0.3684009              B1
##       bucket2008.fctr bucket2009.fctr.predict.Final
## 3                  B1                            B1
## 89984              B1                            B2
## 89990              B1                            B2
## 90000              B1                            B2
## 90002              B1                            B2
## 90008              B1                            B2
##       bucket2009.fctr.predict.Final.accurate .label
## 3                                       TRUE     .3
## 89984                                  FALSE .89984
## 89990                                  FALSE .89990
## 90000                                  FALSE .90000
## 90002                                  FALSE .90002
## 90008                                  FALSE .90008
##        age alzheimers arthritis cancer copd depression diabetes
## 89984   72          0         1      0    0          0        1
## 89990   86          0         0      0    0          1        1
## 90000   80          0         0      0    0          0        1
## 125189  84          0         0      1    0          1        1
## 307454  66          0         0      0    0          0        0
## 307456  99          0         0      0    0          0        0
##        heart.failure ihd kidney osteoporosis stroke reimbursement2008
## 89984              1   1      1            0      0              2420
## 89990              1   1      1            0      0              2530
## 90000              1   1      1            0      0              2720
## 125189             1   1      1            0      0             62830
## 307454             0   0      0            0      0                 0
## 307456             0   0      0            0      0                 0
##        bucket2008 reimbursement2009 bucket2009     .rnorm bucket2009.fctr
## 89984           1                 0          1  1.9883004              B1
## 89990           1                 0          1 -0.5157387              B1
## 90000           1                 0          1  0.8442956              B1
## 125189          5               210          1  0.8591767              B1
## 307454          1              3000          2 -0.3857694              B2
## 307456          1              3000          2  0.6728828              B2
##        bucket2008.fctr bucket2009.fctr.predict.Final
## 89984               B1                            B2
## 89990               B1                            B2
## 90000               B1                            B2
## 125189              B5                            B2
## 307454              B1                            B1
## 307456              B1                            B1
##        bucket2009.fctr.predict.Final.accurate  .label
## 89984                                   FALSE  .89984
## 89990                                   FALSE  .89990
## 90000                                   FALSE  .90000
## 125189                                  FALSE .125189
## 307454                                  FALSE .307454
## 307456                                  FALSE .307456
##        age alzheimers arthritis cancer copd depression diabetes
## 307448  80          0         0      0    0          0        0
## 307454  66          0         0      0    0          0        0
## 307455  66          0         0      0    0          0        0
## 307456  99          0         0      0    0          0        0
## 307457  66          0         0      0    0          0        0
## 449953  80          1         0      0    1          1        1
##        heart.failure ihd kidney osteoporosis stroke reimbursement2008
## 307448             0   0      0            0      0                 0
## 307454             0   0      0            0      0                 0
## 307455             0   0      0            0      0                 0
## 307456             0   0      0            0      0                 0
## 307457             0   0      0            0      0                 0
## 449953             1   1      1            0      1            221640
##        bucket2008 reimbursement2009 bucket2009     .rnorm bucket2009.fctr
## 307448          1              3000          2 -1.2520078              B2
## 307454          1              3000          2 -0.3857694              B2
## 307455          1              3000          2  0.2479826              B2
## 307456          1              3000          2  0.6728828              B2
## 307457          1              3000          2  0.3236726              B2
## 449953          5             34130          4 -0.7428332              B4
##        bucket2008.fctr bucket2009.fctr.predict.Final
## 307448              B1                            B1
## 307454              B1                            B1
## 307455              B1                            B1
## 307456              B1                            B1
## 307457              B1                            B1
## 449953              B5                            B2
##        bucket2009.fctr.predict.Final.accurate  .label
## 307448                                  FALSE .307448
## 307454                                  FALSE .307454
## 307455                                  FALSE .307455
## 307456                                  FALSE .307456
## 307457                                  FALSE .307457
## 449953                                  FALSE .449953

tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn, 
    replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "data.new.prediction")), flip_coord=TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0 
## 2.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction   firing:  model.selected 
## 3.0000    3   0 2 1 0 
## 3.0000   multiple enabled transitions:  model.final data.training.all.prediction data.new.prediction     firing:  data.training.all.prediction 
## 4.0000    5   0 1 1 1 
## 4.0000   multiple enabled transitions:  model.final data.training.all.prediction data.new.prediction     firing:  model.final 
## 5.0000    4   0 0 2 1 
## 6.0000    6   0 0 1 2

#print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())

Null Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.

##                   chunk_label chunk_step_major chunk_step_minor   elapsed
## 10      fit.data.training.all                6                0  9768.275
## 12           predict.data.new                7                0 10395.511
## 9                  fit.models                5                0    71.848
## 11      fit.data.training.all                6                1  9789.672
## 6            extract_features                3                0    26.774
## 4         manage_missing_data                2                2    16.418
## 2                cleanse_data                2                0     7.036
## 7             select_features                4                0    28.472
## 8  remove_correlated_features                4                1    29.537
## 5          encode_retype_data                2                3    16.992
## 3       inspectORexplore.data                2                1     7.066
## 1                 import_data                1                0     0.003
##    elapsed_diff
## 10     9696.427
## 12      605.839
## 9        42.311
## 11       21.397
## 6         9.782
## 4         9.352
## 2         7.033
## 7         1.698
## 8         1.065
## 5         0.574
## 3         0.030
## 1         0.000
## [1] "Total Elapsed Time: 10,395.51 secs"

## R version 3.1.3 (2015-03-09)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.3 (Yosemite)
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] tcltk     grid      stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] randomForest_4.6-10 rpart.plot_1.5.2    rpart_4.1-9        
##  [4] caret_6.0-41        lattice_0.20-31     reshape2_1.4.1     
##  [7] sqldf_0.4-10        RSQLite_1.0.0       DBI_0.3.1          
## [10] gsubfn_0.6-6        proto_0.3-10        plyr_1.8.1         
## [13] caTools_1.17.1      doBy_4.5-13         survival_2.38-1    
## [16] ggplot2_1.0.1      
## 
## loaded via a namespace (and not attached):
##  [1] bitops_1.0-6        BradleyTerry2_1.0-6 brglm_0.5-9        
##  [4] car_2.0-25          chron_2.3-45        class_7.3-12       
##  [7] codetools_0.2-11    colorspace_1.2-6    compiler_3.1.3     
## [10] digest_0.6.8        e1071_1.6-4         evaluate_0.5.5     
## [13] foreach_1.4.2       formatR_1.1         gtable_0.1.2       
## [16] gtools_3.4.1        htmltools_0.2.6     iterators_1.0.7    
## [19] knitr_1.9           labeling_0.3        lme4_1.1-7         
## [22] MASS_7.3-40         Matrix_1.2-0        mgcv_1.8-6         
## [25] minqa_1.2.4         munsell_0.4.2       nlme_3.1-120       
## [28] nloptr_1.0.4        nnet_7.3-9          parallel_3.1.3     
## [31] pbkrtest_0.4-2      quantreg_5.11       RColorBrewer_1.1-2 
## [34] Rcpp_0.11.5         rmarkdown_0.5.1     scales_0.2.4       
## [37] SparseM_1.6         splines_3.1.3       stringr_0.6.2      
## [40] tools_3.1.3         yaml_2.1.13